Jobs ML / AI

We are currently looking for a Senior AI Platform and Model Operations Engineer to join a professional German-based product company team that designs innovative eHealth solutions for hospitals.

You will work on a stable healthcare product.

You'd own the models and the platform around them, serving every channel we put in front of a patient.

The phone assistant is first because it's already underway, but it isn't the point of the job. Next is a chat assistant in the patient portal that can answer questions and walk someone through booking an

appointment, and after that whatever channel comes along.

The thing you're actually building is one internal API that every channel calls, so that the rules about what a patient can do live in one place instead of being written three times, slightly differently. The phone assistant and the web chat are two front ends onto the same thing.

Alongside that: moving off the vendor. Right now we pay per minute for speech recognition and language models, and within about a year we want to be running them ourselves — because we're dealing with

patient data, and because we don't want a vendor swapping a model underneath us without telling us.

To be clear about scope: we mean serving open-weight models and adapting them to our domain. We don't mean training a model from scratch.

One thing we want to be direct about. We are not looking for someone to build what we've already decided on. We don't yet know whether running these models ourselves is the right call — we don't have an accuracy figure on real German phone audio, and we don't have a proper cost comparison. Your first job is to produce both, and if they say we should keep paying the vendor for another year, we need you to tell us that plainly. Someone who quietly builds the thing because we asked for it is worse than no hire at all. If being the person who says "the numbers don't support this" makes you uncomfortable, this isn't the job.

You need

• To have made a language model available behind an API that more than one product called, and to have kept it running: streaming responses, timeouts, retries, and errors that tell the caller something useful.
• To have run a speech recognition model or a language model in production, on hardware that you were responsible for and paid for, with a serving stack such as vLLM or SGLang. We do not mean a notebook or another company's API. You can tell us how many users one GPU served at the same time, what an hour of audio or a million tokens cost, and at what load it started to fail. You can tell us about one outage and what you changed after it.
• Tool calling in production. The assistant will guide patients to book an appointment. The model proposes an action, and our code decides whether the action is permitted. If you built a system where the model was trusted to act, we want to talk about what you would do differently.
• A habit to be suspicious of your own benchmark numbers, especially the good ones. You measured the accuracy of a model on your own data yourself. Speech recognition accuracy on recorded audio counts most, because it is the first task here.
   

Nice to have

• To have handled untrusted text that reaches a model which can trigger actions. A chat box on a patient portal is much easier to attack than a phone call, and someone will try.
• Experience to ground a model on your own content, so that it does not invent things, and honesty about where that still fails.
• Experience to fine-tune or adapt an open-weight model, with LoRA, PEFT or an equivalent method, and the honesty to say whether it was worth the effort. This work comes later. It is not a condition to start.
• Quantization, and an opinion on how much accuracy it costs you.
• Audio processing: sample rates, codecs, and what a phone network does to a recording.
• GPU scheduling, Kubernetes.
• Experience with a system where the same assistant had to behave consistently in more than one channel.
• Experience at a company that took GDPR seriously, and did not only mention it sometimes.
• To know that training a model on patient conversations is a different legal question from processing the conversations to answer the patient. We will give you written rules and a review step for everything that moves patient data. A candidate who does not care about patient data is still not a fit.
   

You do not need

• To have built a voice assistant. That is the other role. The phone assistant is the first consumer of what you build, and its narrowband audio is the first thing that you will measure against. But we buy and configure the telephony, we do not build it, and nobody expects you to have operated it.
• To speak German.
• To know PHP. You would define what our backend must provide. Another team builds it.

We are currently looking for a Senior Voice AI Engineer, Evaluation and Reliability to join a professional German-based product company team that designs innovative eHealth solutions for hospitals.

You will work on a stable healthcare product.

Senior level, and we mean it. You would own the evidence that this system is safe to put in front of

patients, on your own judgement, and tell us when the answer is no. That isn't a job we can hand to

someone still learning what to measure.

You would not be building a voice stack. We use a third-party voice platform that already handles the

telephony, the speech recognition and synthesis, and the knobs for end-of-speech detection and

interruption. What it does not do is tell us whether any of that is working well enough for patients, and it

gives us no way to check that a change hasn't made things worse. That's the job.

So you'd own the conversation and the evidence about it: how the assistant behaves when a caller

interrupts, corrects themselves or won't answer; how fast the whole thing responds once a round trip to

our backend is in the loop; and the body of test cases we run before every change.
 

You need

• To have operated a phone or voice assistant that answered real calls from real users. You can tell us its response time and how you measured it. Give the response time as a percentile, the figure that most callers experienced or worse, not as an average. We want to know that you know what to measure and what good results look like.
• To have built a part of a real-time voice pipeline in your own code, or to have changed one in an important way: streaming audio, end-of-speech detection, turn-taking or interruption. A framework such as Pipecat or LiveKit Agents is acceptable. Changing the settings of a closed platform is not enough for this point. In the first months, about half of the role is building our platform.
• To have tuned a voice system, not only delivered one: end-of-speech detection, barge-in, and how long to wait before the system assumes that the caller has finished. You would do this first with a vendor's settings, and later in our own platform. You would do it on phone audio, where clean barge-in is difficult.
• To know what happens to speech recognition accuracy when the audio comes through a phone line and not through a good microphone. You have measured this yourself. You did not only read about it.
• A way to test conversation changes that does not depend on listening to a few calls and hoping that they are good. This is the centre of the role. It can be a replay harness, a scored scenario set or another form. We want to hear how it worked and where it was not good enough.
• Enough Python to build that harness and the tools around it.
   

Nice to have

• Experience with a closed voice platform and the problems that it brings, not only with your own system.
• Telephony background: SIP, WebRTC, Asterisk, Twilio or similar. It helps you understand why calls sound the way they do, but you would not operate any of it.
• Work with health data, or in another field where mistakes have consequences.
• German, but we do not depend on it.

You do not need

• To speak German.
• To know PHP or our existing backend. Another team builds the endpoints that you would call.
• To have trained a model from the beginning, without a pre-trained model as the base.

About us
Next Dialog automates business communication with AI. Our omnichannel platform handles customer conversations across phone, SMS and messaging: answering questions, booking appointments, qualifying leads and syncing everything to the CRM, 24/7. Our clients range from HVAC installers and real estate brokerages to dental clinics, logistics and energy companies across Poland, Germany, the rest of Europe and the US. We're a small, product-focused team working at the intersection of AI and communication automation, and we're hiring a Forward Deployed AI Engineer to own our AI systems from architecture to live production.

The role
You own AI systems end to end, from architecture to live production. You will:

• Build harnesses: test and eval harnesses for LLM and voice pipelines, simulated conversations, and regression suites that catch quality drops before clients see them
• Instrument what ships: tracing of LLM and agent calls (prompts, tool calls, tokens, cost, latency), quality monitoring and regression alerts in production
• Design and build microservices: new services and integrations that fit into a platform running in production, including MCP servers and tools for our agents and internal tooling
• Optimize AI pipelines: latency, cost, accuracy, and reliability, measured with real metrics
• Automate everything repetitive: checks, deployments, agent setup, and routine ops, using scripts, agents, and MCP tooling
• Design for safety and privacy: prompt injection, PII handling, call-recording consent, human approval for irreversible actions
• Own live production: monitor, debug, and fix real campaigns running real calls

What the role is not

• Not a research role. No model training from scratch, no TensorFlow/PyTorch.
• Not a prompt-only role. Prompts are one tool among evals, tracing, integrations and ops.
• Not a frontend role. React/Next.js is in the stack, but it is not where you will spend your days.

Our stack
TypeScript, NestJS, PostgreSQL, React, Next.js · Retell AI, LiveKit, Deepgram, ElevenLabs, Twilio · Gemini/Vertex AI, OpenAI, Anthropic · Langfuse / OpenTelemetry-style tracing · GCP, Terraform, Docker · Cursor, Claude Code, MCP

Must have

• 4+ years of engineering experience in any server-side language, with a strong track record of designing, running and supporting microservice architectures in live production
• Production experience with LLMs: prompting, structured output, tool calling, RAG, evals
• Experience building test or eval harnesses and automating pipelines
• Experience with LLM observability: tracing prompts, tool calls, tokens, cost and latency, and using traces to find and fix regressions
• AI-native workflow: you do most of your tasks through AI agents and know how to build context for them
• Hands-on experience with MCP: you have built or wired MCP servers and tools, not only used them
• Strong SQL: comfortable analysing call and campaign data in PostgreSQL
• Experience with real-time or webhook-driven systems
• You design for safety and privacy: prompt injection, PII, consent, guardrails
• English B2+

Nice to have

• Voice AI experience (Retell, Vapi, LiveKit, Pipecat, telephony, SIP/WebRTC, streaming audio, STT/TTS, turn-taking and interruptions, latency tuning)
• Past experience as a team or tech lead
• Forward deployed or solutions engineering experience at a startup
• GCP, Terraform, on-prem deployments

Hiring process

1. Online prescreen
2. Interview
3. Decision

Description

Join GlobalLogic’s Presales Team within the Healthcare Business Unit, supporting clients across the EMEA region. This strategic role focuses on engaging new clients, solving real-world healthcare challenges, and launching data-driven AI/ML projects. You will work closely with clients and internal stakeholders to translate complex business needs into impactful solutions. If you’re passionate about applying data science in a meaningful domain and want to shape the future of healthcare, we’d love to hear from you.

Requirements

• Bachelor’s or Master’s degree in Software Engineering, Data Science, Computer Science, Statistics, Applied Mathematics, or related field.
• 5+ years of experience in Senior Engineer or Lead Engineer or Tech Lead roles, ideally with exposure to healthcare projects.
• Good expertise and skills using Claude/Codex or equivalent AI tools to do research, planning, software development and testing.
• Proficiency in Python and/or any other programming language (C/C++, JAVA, C#, etc)
• Strong communication and visualization skills for storytelling with data.
• Familiarity with cloud environments (Azure, AWS, or GCP) and their native services.
• Experience with LLM and GenAI constraints and limitations and how to overcome those.
• English proficiency at Advanced level.
• Good presentation and demonstration skills

Preferred Qualifications (Nice to Have)

• Experience working with medical data (EHR, imaging, wearables, clinical trials, etc.).
• Familiarity with healthcare regulations related to data and AI (e.g., HIPAA, GDPR, FDA AI/ML guidelines).
• Knowledge of FHIR, HL7, or other healthcare interoperability standards.
• Involvement in presales, proposal writing, or technical advisory work.
• Experience working with data ingestion and data processing frameworks
• Understanding of MLOps/AIOps concepts and tools.

Job responsibilities

• Lead the design and development of solutions across HealthTech and MedTech projects.
• Be comfortable with ambiguity, onsite/embedded work, have willingness to travel and work directly at the customer’s environment, building solutions under a hard deadline with incomplete requirements.
• Participate in technical presales by analyzing business cases and identifying opportunities for AI/ML application.
• Lead Discovery and/or Advisory projects, understand cusmoter’s needs, collect inputs and painpoints from the customer and create working SW prototypes within limited time to demonstrate feasibility & value.
• Collaborate with software engineers, cloud architects, and QA to further develop prototypes into scalable production systems.
• Contribute to the development of GlobalLogic’s healthcare-focused AI accelerators and reusable components.
• Present technical solutions to clients, both business and technical audiences.
• Ensure adherence to privacy, security, and compliance standards for data and AI usage.
• Author clear documentation and contribute to knowledge sharing within the team.

Format: Remote | Full-time / Contract
Project: Confidential AI Product Development

🚀 Key Project Goal: Deliver a working prototype within 8–12 weeks.

Within this timeframe, you will develop the AI interaction architecture powering five distinct interactive characters, fully integrated with the 3D simulation environment. The prototype should support real-time voice conversations, context-aware responses, character-specific behaviors, and AI-driven actions that trigger corresponding animations and interactions. The architecture should be reusable and designed to scale to 60+ characters and additional interaction scenarios.

About the Project

We are developing an AI-powered interactive simulation platform featuring intelligent digital characters capable of natural conversations and context-aware behavioral responses.

We are looking for an engineer experienced in real-time AI systems, conversational agents, multimodal interaction, and event-driven architectures.

Responsibilities

• Design and implement real-time AI interaction pipelines.
• Integrate speech-to-speech, speech-to-text (STT), text-to-speech (TTS), and low-latency conversational AI services.
• Build AI agents capable of understanding user intent and triggering structured actions.
• Develop character behavior logic using state machines, rules, and LLM orchestration.
• Connect conversational AI with external 3D animation and simulation engines.
• Implement configurable character profiles, memory, and interaction scenarios.
• Build backend services, APIs, and interaction logging.
• Evaluate third-party AI services and SDKs to accelerate prototyping.

• Collaborate with 3D developers to establish reliable communication between AI systems and interactive characters.

   

Required Experience

• Strong experience with Python or TypeScript / Node.js.
• Hands-on experience integrating LLM APIs into production applications.
• Familiarity with streaming APIs, WebRTC, WebSockets, and event-driven systems.
• Experience with AI agent frameworks, function calling, and structured outputs.
• Understanding of state management and conversational context.
• Experience with cloud infrastructure, databases, and backend development.

• Ability to design scalable architectures and deliver working prototypes within defined timelines.

   

Nice to Have

• Experience with OpenAI Realtime API, LiveKit, ElevenLabs, or Azure Speech.
• Familiarity with Convai, Inworld AI, or conversational NPC systems.
• Experience integrating real-time avatars or game engines.
• Experience building multimodal AI applications.
• Familiarity with agent evaluation and automated testing.

Initial Project Goals

Develop the AI interaction architecture for a prototype featuring five distinct digital characters, with reusable logic that can support dozens of additional characters and scenarios.

The initial prototype is expected to be delivered within 8–12 weeks.

We value engineers who can make sound architectural decisions, leverage existing technologies effectively, and balance implementation speed with scalability and reliability.

About us

TR Labs is a technology company helping B2B clients across Europe turn complex business

problems into reliable, intelligent software.

In this role, you’ll join the engineering team of a global telecom technology company as a fully

integrated member, with TR Labs as your employer and support.

What you’ll do

• Design, build, and ship production-grade software within the client’s platform, primarily in Java

• Develop and evolve backend services, integrations, and data flows

• Contribute to AI/ML and LLM-based initiatives as the product roadmap requires

• Work with product and engineering stakeholders to turn business problems into reliable technical solutions

• Write clean, tested, well-documented code and take part in code reviews and architecture discussions

What we’re looking for

• 4+ years of professional software engineering experience, with Java as your main language

• Proven hands-on experience with AI/ML and LLM-based solutions (e.g. model integration, RAG,

agents, vector databases, OpenAI/Anthropic APIs)

• Experience with cloud platforms (AWS, GCP, or Azure), Docker, and CI/CD

• Solid knowledge of SQL and data handling at scale

• Fluent English, written and spoken

• Ability to work independently in a client-facing, distributed team

Nice to have

• Python, plus experience with other languages such as Go or TypeScript

• Telecom domain experience (roaming, billing, signaling, network data)

• MLOps experience (MLflow, Kubeflow, SageMaker, or similar)

• Kubernetes and microservices architecture

About ChatRevenue.ai 

ChatRevenue.ai was founded by Ratmir Timashev (founder of Veeam) and Vlad Voskresensky (founder of Revenue Grid). 

We are building an agentic sales automation for post-CRM era. The AI-native product combines structured and unstructured data, LLM workflows, and agentic automation to help sales people to focus properly, work more efficiently and automate repetitive sales processes. 

The product is already being tested by early customers. We are growing the core engineering team and looking for people who want to work on real product systems with direct impact on architecture, quality, and speed of delivery. 

About the role 

We are looking for a Senior Generative AI Engineer with 5+ years of commercial Python experience and hands-on experience building production GenAI / RAG systems. 

This is a hands-on engineering role focused on: 

• Python backend  
• LLMs and RAG  
• AI Copilot / assistant workflows  

We are looking for someone who can take a problem, propose a practical solution, and move it to production. 

What you will do 

• Design, build, deploy, and maintain production GenAI / RAG pipelines  
• Integrate LLMs and agentic assistants into product workflows  
• Improve latency, reliability, scalability, and cost-efficiency of AI systems  
• Evaluate and implement new GenAI, multi-agent, and orchestration frameworks  
• Contribute to architecture and technical decisions across the platform  

What we are looking for 

• 5+ years of commercial Python experience 
• Strong backend experience with FastAPI, APIs, webhooks, Docker, Kubernetes, Git, and CI/CD 
• Commercial experience with LLMs in production 
• Strong hands-on experience with RAG, retrieval workflows, embeddings, and vector search 
• Experience with LangChain, LangGraph, LangSmith, or similar frameworks 
• Good knowledge of SQL, Pandas, ETL, data cleaning, and ingestion pipelines 
• English: B2 or higher 
• High ownership, independence, and product mindset 

Nice to have 

• Multi-agent LLM systems 
• Knowledge graphs, graph databases, or graph-based retrieval 
• Kafka, Azure Event Hub, or other event-driven systems 
• Azure cloud infrastructure 
• Production ML / NLP systems 
• Evaluation, tracing, observability, and optimization of AI pipelines 
• Graph algorithms or tools like NetworkX 

What we offer 

• Remote-first setup 
• Flexible schedule aligned with European time zones 
• A core engineering role in an AI-native product 
• Direct influence on technical decisions and architecture 
• Real ownership and visible product impact 

If you have strong Python backend fundamentals and real production GenAI / RAG experience, we’d love to talk. 

We are looking for a Full Stack Engineer to join enterprise AI projects focused on intelligent automation, AI integrations, and modern internal platforms.
You’ll work on scalable web applications powered by Python, React, cloud technologies, and LLM-based features.

Requirements

• Strong experience with Python
• Experience with FastAPI or similar backend frameworks
• Commercial experience with React.js
• Experience building REST APIs and full stack applications
• Familiarity with cloud platforms (preferably Azure)
• Understanding of scalable architectures and software engineering best practices

Nice to Have

• Experience with AI/LLM integrations
• Familiarity with LangChain, LangGraph, or AI agent frameworks
• Experience with Azure OpenAI or similar AI services
• TypeScript, Docker, CI/CD experience
• Experience with async processing, queues, or event-driven systems

Responsibilities

• Develop and maintain full stack applications
• Build backend services and frontend interfaces
• Integrate AI capabilities into enterprise products
• Collaborate with engineering and product teams
• Contribute to architecture, deployment, and performance improvements

Responsibilities:

• Own and support core ML infrastructure (MLflow, CVAT, GPU/CPU cloud instances, internal data tools).
• Assist Data Engineers with deploying and maintaining data pipelines.
• Act as the primary contact for ML and Data teams on infra-related requests.
• Improve reliability and efficiency by adding automation, monitoring, and tooling enhancements.
   

Must have:

• Knowledge of Python, Terraform, Docker, Linux.
• Hands-on experience with cloud environments (Azure prefered).
• Understanding of the end-to-end ML lifecycle (data → training → evaluation → deployment → monitoring).

Nice to have:

• Familiarity with Kubernetes.
• Experience with FFmpeg or GStreamer for video processing and streaming.
• Experience with model serving frameworks and inference formats (ONNX, TensorRT, OpenVINO, NVIDIA Triton).
• Experience deploying computer vision models, especially to edge/embedded devices (NVIDIA Jetson, Intel platforms, Raspberry Pi).

• Надаємо бронювання від мобілізації

Applefy, a leading mobile and web development agency, is seeking an AI Engineer to join our team on an ongoing project.

The project is an AI customer-support agent for an e-commerce company. You will build the agent that answers customers from the client's own knowledge base, looks up live order and account data, and hands off to human agents in Zendesk with full context. 

This suits an engineer who wants to ship production AI, not just prototypes, and who cares about accuracy and safety as much as speed.
 

What We Offer:

• Flexible work schedule: Manage your own time and work at your own pace.
• Fully remote: Work from anywhere in the world.
• Comprehensive leave policy: All official holidays, 20 days of paid vacation, and 10 sick days without needing a doctor's note. [confirm]
• Hands-on product work: You will work directly with the founder and the client team, with weekly syncs and a demo at the end of each milestone.
   

Key Requirements:

• 4+ years of backend engineering experience, with strong Python (FastAPI or similar)
• Hands-on experience building LLM features in production, including tool calling, prompt design and output evaluation
• Practical RAG experience: chunking, hybrid search, source citations, and knowing when a model should decline to answer
• Experience integrating with third-party REST APIs, including authentication, retries and error handling
• Automated testing discipline, with unit and integration coverage targets you can explain
• A working understanding of GDPR and of protecting personal data in logs and prompts 
• Strong communication and soft skills
• Proficiency in spoken and written English
• Keen attention to detail
• A desire to learn and grow professionally
   

Preferred Qualifications:

• Experience with the Zendesk or Notion APIs
• Experience with LLM tracing and evaluation tools such as Langfuse
• Experience with prompt-injection defence or red-teaming
• Experience with EU cloud hosting and PostgreSQL with pgvector
• A React background for an embeddable chat widget
   

How to apply: Send a short note on the most relevant agent or RAG system you have built, a link to code or a demo if possible, and your availability. 
 

• Our client is an energy solutions provider. Our AI-enabled platform supports operations, tracking and decision-making to optimize large-scale renewable and traditional energy portfolios. We're delivering a key client MVP in February 2027, and the project continues into 2028. Our stack: Ruby on Rails, Node.js/TypeScript and Python services, PostgreSQL, Snowflake and AWS. We're looking for an applied AI engineer to build AI with real, measurable benefit across our operations and products.

   

• Responsibilities:

  Work closely with product teams to help define and execute technical criteria
  Find high-value uses for AI and define how success is measured
  Design, build and ship LLM-powered features and agents from prototype to production, such as automating client data onboarding
  Own retrieval and RAG over our data, building on the foundation our data engineers prepare
  Build evals and monitoring that track quality, cost and latency, and improve systems based on the results
  Help refine our customer-facing AI features

   

• Mandatory Skills Description:

  Nearshore strongly preferred; otherwise at least 4 hours of daily overlap with US Pacific Time

  Mid-to-senior software engineer who has shipped LLM-based features or agents to production, not only prototypes
  Production experience in Python (preferred) or Node.js/TypeScript
  Building with LLMs: prompt and tool design, structured outputs, retrieval/RAG and agent orchestration
  Evaluating AI systems: eval sets built from representative data, automated graders, human review and production monitoring
  AWS or another enterprise cloud platform, including its AI services (AWS Bedrock preferred)
  AI-native development: daily use of agentic AI coding tools, and experience scaling shared AI workflows across a team and into the development lifecycle, including CI/CD
  Strong English and communication skills

   

• Nice-to-Have Skills Description:

  Agent tool protocols such as MCP
  SQL and cloud warehouse data (Snowflake preferred)
  Infrastructure as code (e.g. Terraform)
  Exposure to energy, utilities or sustainability data

   

• Languages:
  • English: B2 Upper Intermediate

Solid Software is looking for an AI Software Architect/ Senior AI Engineer to design and deliver production-grade solutions powered by LLMs. The role requires deep expertise in architecting, building, and operating complex AI systems: RAG, agentic workflows, tool calling, evaluation frameworks, and observability platforms.

The ideal candidate pairs strong software engineering fundamentals with a track record of shipping real-world AI products. Beyond experimentation, you can design scalable, reliable, and cost-effective LLM solutions that run successfully in production, with a solid grasp of modern AI architecture patterns, prompt engineering, retrieval systems, and agent orchestration.

What will you do?
 

• Own the architecture of AI-powered services and ensure their effective integration with backend, mobile, and web applications.
• Design, build, and maintain production-grade LLM applications using modern AI frameworks and orchestration platforms.
• Lead technical design reviews and drive architectural decisions across AI services, backend systems, data pipelines, and cloud infrastructure.
• Design and implement agentic workflows involving tool use, function calling, multi-agent systems, planning, memory management, and reasoning chains.
• Design and optimize RAG architectures using vector databases, embeddings, document processing pipelines, and retrieval optimization techniques.
• Establish evaluation frameworks to assess AI quality, reliability, latency, cost, and business impact, including benchmarking and automated quality assessment.
• Implement AI observability and monitoring using tools such as Langfuse, LangSmith, OpenTelemetry, and similar platforms.
• Optimize prompts, retrieval strategies, model selection, and system architecture to improve accuracy, performance, reliability, and cost efficiency.
• Design scalable APIs and services that expose AI capabilities to internal and external applications, and deploy them on cloud platforms, primarily Google Cloud Platform (GCP).
• Collaborate with software engineers, data scientists, product managers, and domain experts to translate business requirements into practical AI solutions.
• Define AI engineering standards, best practices, and governance processes to support the development and maintenance of reliable AI systems.
• Mentor engineers working on AI initiatives and contribute to architectural decisions and technical direction.
• Document AI architectures, workflows, evaluation methodologies, and operational procedures to ensure maintainability and knowledge sharing.

Requirements:
 

• 10+ years of software engineering experience, including 5+ years building LLM-based and generative AI solutions in production.
• Upper-Intermediate English (B2 or C1) for comfortable verbal communication within an international team.
• A Master's degree in Computer Science, Artificial Intelligence, Machine Learning, Software Engineering, or a related field.
• Strong Python development experience and software engineering fundamentals.
• Hands-on experience with AI orchestration frameworks such as LangGraph, LangChain, LlamaIndex, CrewAI, or equivalent technologies.
• Practical experience designing and implementing agentic systems involving tool use, function calling, planning, memory management, and multi-agent workflows.
• Strong expertise in RAG architectures, vector databases, embeddings, prompting strategies, context management, and retrieval techniques.
• Experience with AI evaluation and observability tools such as Langfuse, LangSmith, or similar platforms, including benchmarking, monitoring, and automated quality assessment.
• Experience working with structured and unstructured data pipelines and databases, including PostgreSQL, vector databases, and document stores.
• Production deployment experience with Docker, Kubernetes, cloud platforms, and CI/CD pipelines.
• Ability to evaluate architectural trade-offs involving model performance, latency, reliability, maintainability, and cost.
• Strong communication, technical leadership, and mentoring skills, with the ability to collaborate effectively across engineering, data, and product teams.
• Experience with mobile application development or architecture is a strong advantage.

Nice to have:

• Experience working with commercial and open-source models, including OpenAI, Anthropic, Gemini, Llama, and Mistral, as well as fine-tuning techniques.
• Experience in regulated, privacy-sensitive, or high-availability environments.
• Experience integrating AI capabilities into mobile and web applications.
• Familiarity with synthetic data generation and model optimization techniques.

Why Solid Software?

• We support Ukraine; 
• A wonderful team of enthusiasts and like-minded people;
• Atmosphere of comfortable work & life balance;
• Flexible schedule and work-from-home opportunity;
• Opportunity to work on projects with different directions (web, mobile, tablet, watch, desktop);
• MacBook Pro and great tech;
• Decent compensation;
• Lack of bureaucracy;
• Compensation for professional events, sports activities;
• Corporate online parties and fun team-buildings;
• Anniversary and birthday presents.

Professional growth:

• Onboarding and mentorship program with a dedicated onboarding guide built by our team;
• Internal Career Path program from Developer to Architect that consists of knowledge areas for IT specialists, books, training sessions, and work activities;
• Corporate library with the best programming books (+ an ability to order a particular book if we don't have it);
• Professional and constant feedback.

We truly value the unique talents and skills that each individual brings to our company. As we continue on our journey of growth, we eagerly anticipate the opportunity to welcome you into our team. 

Apply now and help us shape what comes next!

PwC is a global network of more than 370,000 professionals in 149 countries that turns challenges into opportunities. We create innovative solutions in audit, consulting, tax and technology, combining knowledge from all over the world.

PwC SDC Lviv, opened in 2018, is part of this global space. It is a place where technology is combined with team spirit, and ambitious ideas find their embodiment in real projects for Central and Eastern Europe.

What do we guarantee?

• Work format: Remote or in a comfortable office in Lviv - you choose.
• Development: Personal development plan, mentoring, English and Polish language courses.
• Stability: Official employment from day one, annual review of salary and career prospects.
• Corporate culture: Events that unite the team and a space where everyone can be themselves.

PwC Digital team is a modern software house within a large consulting company. We combine start-up dynamics and innovation with the extensive opportunities of PwC’s global network.

Join us and seize the opportunity to work with cutting-edge technologies such as GenAI, AI agents & copilots, cloud computing, chatbots, data lakes, and advanced machine learning algorithms, all built on a foundation of modern application development across diverse frontend and backend technologies.

Your future role

As an AI Engineer (Python + GenAI), you will:

• Develop innovative solutions, both internally and for our PwC US clients, leveraging cutting-edge machine learning and AI technologies.
• Develop intelligent assistants using the latest Large Language Models (LLMs), such as GPT-4, Falcon 2, Llama 3, and Mixtral.
• Apply Retrieval-Augmented Generation (RAG) techniques and agent frameworks such as LangGraph and CrewAI.
• Use your AI and software engineering expertise to recommend effective technical approaches and solution architectures for addressing business challenges.
• Participate in software deployments and work closely with PwC industry experts and users to solve ongoing issues.
• Collect, preprocess, and analyze large datasets for training AI models.
• Contribute to project documentation.

Apply if you have:

• 3+ years of relevant professional experience.
• Professional experience building applications using Large Language Models (LLMs) and related concepts and design patterns, including RAG, vector databases, embeddings, prompt engineering, and multi-agent systems.
• Familiarity with LLM frameworks such as LangChain and LlamaIndex.
• Strong knowledge and experience in production-grade Python development.
• Understanding of cloud concepts and architectures, with hands-on experience using Azure/GCP/AWS.
• Experience with containerization tools such as Docker and Kubernetes.
• Expertise in data manipulation in Python using tools such as Pandas and NumPy.
• Proficiency in English, both written and spoken.
• Excellent analytical and problem-solving skills.

Nice to have:

• Knowledge of frameworks and Python libraries related to GenAI agents, such as LangGraph, CrewAI, AutoGen, and TaskWeaver.
• Knowledge of Natural Language Processing (NLP) techniques.
• Knowledge of an additional programming language, such as C#, Go, or Java.

Policy statements:
https://www.pwc.com/ua/uk/about/privacy.html

We are looking for a Computer Vision Engineer to join our team and develop vision-based systems for UAV applications.
In this role, you will work on the development of computer vision algorithms and their deployment on resource-constrained devices. You will be involved across the development cycle, from algorithm development and hardware evaluation to testing, performance optimization, and deployment.
This is a product-facing role where you will work closely with stakeholders from multiple departments and teams to develop reliable vision-based solutions for real-world applications.
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We are looking for a Computer Vision Engineer to join our team and develop vision-based systems for UAV applications.
In this role, you will work on the development of computer vision algorithms and their deployment on resource-constrained devices. You will be involved across the development cycle, from algorithm development and hardware evaluation to testing, performance optimization, and deployment.
This is a product-facing role where you will work closely with stakeholders from multiple departments and teams to develop reliable vision-based solutions for real-world applications.

Responsibilities:

• Develop and optimize computer vision algorithms for real-time edge applications, including object detection, tracking, and navigation
• Implement and adapt machine learning models and vision frameworks for integration with UAV systems and onboard hardware
• Test and validate algorithms in real-world conditions, fine-tuning models and system parameters to ensure stable performance across different environments
• Contribute to the selection and evaluation of camera sensors, lenses, and onboard computing hardware for vision applications
• Design repeatable testing approaches and data pipelines to measure, benchmark, and continuously improve vision model performance
• Work closely with computer vision, robotics, embedded, and engineering teams to integrate new approaches and technologies into production systems

Requirements:

• Strong experience developing high-performance, real-time applications in C++ or Rust
• Practical experience with ROS2 for distributed systems and OpenCV for real-time image processing, feature detection, or related computer vision tasks
• Hands-on experience building and deploying computer vision solutions for real-world embedded or robotic applications
• Solid understanding of traditional computer vision methods, including feature extraction, optical flow, and camera calibration
• Strong knowledge of modern deep learning approaches, including CNNs and Vision Transformers
• Experience deploying vision algorithms on robotic, UAV, or other edge platforms
• Strong analytical and problem-solving skills, with the ability to design, test, and improve algorithms under real-world constraints
• Ability to work effectively in multidisciplinary teams with engineers, researchers, and external partners
• Comfortable working in a fast-paced environment with changing priorities and a high degree of ambiguity
• English at B2–C1 level for communication with international colleagues and technical documentation

We Offer:

• Official employment in accordance with Ukrainian labor law
• Mobilization deferment
• Medical insurance
• Individual English lessons
• Stock Options
• Opportunity to work on advanced UAV and autonomous systems
• Professional growth in an international engineering environment

About the Product

We are developing a platform of AI analyst coworkers that deliver industry insights. The agents operate in secure, sandboxed environments and can use tools, communicate across channels, and complete complex tasks with human oversight.

About the Role

You will join a small engineering team building and operating the platform’s agentic AI capabilities. This is a hands-on role covering AI agents, system architecture, production reliability, feature design and secure deployment.

Tech Stack

- TypeScript, Node.js, and Bun

- LLM APIs, agent SDKs, and MCP

- SQL and SQLite

- GCP, including Cloud Run, Cloud Build, and Artifact Registry

- Terraform and CI/CD

- Event-driven messaging and asynchronous processing

- Structured logging, monitoring, and tracing

Key Responsibilities

- Design and build production AI agents that can plan, use tools, maintain context, and complete complex tasks.

- Design scalable systems for agent orchestration, messaging, task execution, permissions, and persistent state.

- Integrate LLM providers, external APIs, databases, and communication channels.

- Build secure, sandboxed environments for executing agent workloads.

- Develop evaluation, monitoring, and debugging tools for agent quality and reliability.

- Deploy and operate AI services on GCP.

- Own technical decisions and establish strong engineering, testing, and security practices.

Required Skills

- 7+ years of software engineering experience.

- Hands-on experience delivering production AI or agentic systems used by real users.

- Strong system design experience, particularly with distributed and event-driven systems.

- Strong backend development skills in TypeScript, Python, Go, Java, Rust, or a similar language.

- Experience with LLM APIs, tool calling, structured outputs, context management, and agent workflows.

- Experience with SQL databases, asynchronous processing, retries, idempotency, and failure handling.

- Production experience with GCP, Docker, CI/CD, and infrastructure as code.

- Strong understanding of application security, permissions, secrets, and workload isolation.

- Experience with observability, incident investigation, and production operations.

- Ability to work independently and communicate clearly in a fast-moving environment.

Nice to Have

- Experience with agent frameworks such as LangGraph, LangChain, CrewAI, OpenAI Agents SDK, Claude Agent SDK, or similar.

- Experience with Terraform, Kubernetes, vector search, RAG, or multi-agent systems.

- Experience with financial research or analyst workflows.

Who We Are

Adélie Cognition builds solutions for a private investment firms and offices powered by latest AI models.