Jobs ML / AI

About us

DroneOps is a company creating innovative UAV-related products, focused on UAV autonomy and navigation, founded in 2023. We are a team of innovators and inventors who take on the duty to help the armed forces of Ukraine defend our country and democracy by providing reliable advanced systems that simplify their work, improve personnel safety, and enable more cost-effective and precise UAV mission execution.

Overview

We are looking for a Middle / Senior Computer Vision / Deep Learning Engineer to extend our onboard navigation capabilities with modern perception and learning-based methods.

This is a hands-on R&D role: you work from research and prototypes to production on edge hardware, collaborate with software, and flight teams, and help shape how new CV/DL features are built and shipped. The role suits someone who can follow state-of-the-art work, implement what fits our constraints, and care about real-world performance—not only offline benchmarks.

Responsibilities

Relocalization & navigation

• Design and develop satellite, map, and semantic navigation capabilities that improve the overall quality of our navigation
• Build and maintain pipelines for map and reference-data preparation before missions
• Extend the existing visual-inertial navigation stack with relocalization and map-based aiding features
• Develop training pipelines for these tasks (data preparation, training, evaluation, and iteration)
• Create utilities that make map preloading and mission setup convenient for operators and integrators
•  

Research & implementation

• Track relevant research and industry approaches; prototype and adopt methods where they clearly improve the product
• Reimplement or adapt algorithms when off-the-shelf solutions do not fit our data, hardware, or latency requirements
• Define evaluation metrics and acceptance criteria for new features before field and deployment rollouts
•  

Edge deployment & production

• Optimize models and inference for target onboard hardware
• Integrate new features into the production codebase with stable APIs, logging, and failure handling
• Take part in architecture decisions, code review, and team practices that keep production quality high as the stack grows
•  

Required skills & experience

• 3+ years of practical experience in computer vision and deep learning
• Strong Python; ability to write clear, maintainable code for experiments and production paths
• Experience with training and deploying deep learning models (PyTorch)
• Familiarity with model optimization for edge or embedded devices (pruning, quantization, ONNX/TensorRT-class tooling, or equivalent)
• Comfort working with geospatial or map-related data and preprocessing pipelines
• Practical production mindset: reproducible experiments, versioning, and validation on real data
• English B2+ for documentation and technical discussion; Ukrainian for day-to-day team communication
•  

Nice to have

• Experience with deep learning keypoint detection and matching approaches
• Comfortable with geometric computer vision (camera models, epipolar geometry, robust estimation)
• Multi-sensor fusion / SLAM / EKF background
• UAV, robotics, or aerospace field experience
• Experience with satellite or aerial imagery, semantic segmentation, or visual place recognition
•  

What we offer

• Competitive salary
• Reservation from mobilization
• Impact on improving our country's defense capabilities
• 21 days of vacation + public holidays, sick leaves

SupportYourApp is an international Intelligent Support-as-a-Service company that has been providing business process outsourcing services to IT companies around the globe for the past 15 years. We have 1500+ people in our international community.

We operate globally, supporting clients in 60 languages and partnering with industry leaders like MasterCard, Calm, and MacPaw. With international hubs and coworking spaces around the world, we develop innovative products like Quidget and continuously work to improve the customer experience.

About the role:

We are looking for an AI Solutions Engineer to join our team and lead the design and implementation of AI-powered solutions for customer support and business operations.

In this role, you will translate business and operational requirements into scalable technical solutions, design AI architectures, evaluate technologies and integrations, and guide solutions from discovery through implementation.

You will work closely with clients, internal teams, and technical stakeholders to identify the right AI approach for each use case and ensure that solutions are reliable, scalable, and aligned with business goals.

What you will do:

• Design end-to-end AI solutions based on client business and technical requirements;
• Define solution architecture, integrations, data flows, and implementation approach;
• Design and implement AI-powered solutions, including conversational AI, chatbots, agent assist, and automation;
• Work with LLMs, RAG, prompt engineering, AI agents, and knowledge retrieval systems;
• Lead technical discovery and solution design sessions with clients and internal teams;
• Conduct demos of proposed AI solutions and explain their functionality, capabilities, and business value;
• Troubleshoot technical issues and drive solutions through to resolution.

What you need to succeed in this role:

• Fluency in English (C1+ / Advanced+);
• Practical experience taking an AI support solution from implementation/configuration to actual use by customers or support teams;
• Experience implementing AI-powered chatbots, voicebots, or other conversational AI solutions specifically for customer support or customer service operations;
• Hands-on experience with AI and automation platforms (for example, Zendesk AI Agents, n8n, Voiceflow, Botpress, Cognigy, or similar tools);
• Understanding of LLMs, Generative AI, RAG, prompt engineering, and AI agents;
• Solid understanding of APIs, integrations, databases, and system architecture;
• Ability to translate business requirements into scalable technical solutions and communicate technical concepts to both technical and non-technical stakeholders.

Will be an advantage:

• Hands-on experience with helpdesk platforms (for example, Zendesk, Freshdesk, and Intercom), including their automation and AI features;
• Experience with AI evaluation, monitoring, and guardrails;
• Experience working directly with clients during technical discovery and solution design;
• Experience leading implementation projects.

Benefits and Perks:

• Providing services during business hours;
• Opportunity to cooperate fully remotely;
• Inclusive international environment;
• Compensation in USD;
• Rewards for referring friends;
• Balance between project workload and personal time, but also – internal health policy;
• Responsive leadership interested in your growth and long-lasting cooperation;
• Greenhouse conditions for self-development;
• A culture built on trust, with no time-tracking requirements.

*The items listed in this section may vary depending on the terms of your engagement. Certain benefits and conditions typically apply to employees; independent contractors may not be eligible for all of these. The specific terms, including compensation, benefits, and work conditions, will be clearly defined in your agreement if selected.

You can learn more about the company and its culture by visiting our official website and social pages on Facebook, Instagram, and LinkedIn.

Are you passionate about connecting systems and driving intelligent automation? Do you thrive in fast-paced environments where you can analyze workflows, implement AI solutions, and collaborate with teams to deliver scalable, high-impact results?

Grab the chance to join us and send your CV in English, pointing out your outstanding skills!

Know someone perfect for the role? Refer them and get rewarded! 

We adhere to the principles of equal treatment of candidates and prohibit discrimination on any grounds protected by law.

Your personal data will be processed as described in the SupportYourApp Candidate Privacy Notice.

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 put a language model behind an API that more than one product called, and kept it running.
• Streaming responses, timeouts, retries, and errors that tell the caller something useful.
• To have run a speech recognition or language model in production on hardware you were responsible for. Not a notebook, not someone else's API.
• To be able to size and cost it: how many people one GPU will serve at once, what an hour of audio or a million tokens costs you, where it starts to fall over.
• Tool calling in production, and a clear view on where the boundary sits. The assistant will guide patients through booking, which means the model proposes an action and our code decides whether it's allowed. If you've built something where the model was effectively trusted to act, we'd want to talk about what you'd do differently.
• To have dealt with untrusted text reaching a model that can trigger actions. A chat box on a patient portal is a much easier thing to attack than a phone call, and someone will try.
• Experience grounding a model on your own content so it stops inventing things — and honesty about where that still fails.
• Experience fine-tuning or adapting an open-weight model, LoRA or PEFT or something equivalent, and the honesty to say whether it was worth the effort.
• A habit of being suspicious of your own benchmark numbers, especially the good ones.
• To understand that using patient conversations to train a model is a different legal question from using them to answer one. If that distinction is new to you, this role probably isn't the right fit.
• Python and Linux, containers, and a serving stack like vLLM or TGI.
• Enough experience to work without an ML team around you, and to tell a team lead something he'd rather not hear.
• We are considering candidates who work through Ukrainian FOP.

Nice to have

• Quantization, and a view on what it costs you in accuracy.
• Audio processing: sample rates, codecs, what a phone network does to a recording.
• GPU scheduling, Kubernetes.
• Having built something where the same assistant had to behave consistently across more than one channel.
• Having worked somewhere GDPR was taken seriously rather than mentioned occasionally.

You don't need

• To have built a voice assistant, or to know anything about telephony. That's the other role, and the phone channel is only one consumer of what you'd build.
• To speak German.
• To know PHP . You'd define what our backend needs to expose; 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 run a phone or voice assistant that took real calls from real users, and to be able to tell us what its response time was and how you measured it. Having built the audio pipeline yourself is fine but not the point — we care that you know what to measure and what good looks like.
• To have tuned a voice system rather than only shipped one: end-of-speech detection, barge-in, how long to wait before assuming the caller has finished. You'd be doing this against a vendor's settings rather than your own code, and their own documentation says clean barge-in can't be guaranteed on phone audio.
• To know what happens to speech recognition accuracy when the audio comes down a phone line instead of a good microphone, and to have measured it rather than read about it.
• A way of testing conversation changes that doesn't rely on listening to a few calls and hoping. This is the centre of the role. Whatever form it took — a replay harness, a scored scenario set, anything — we want to hear how it worked and where it let you down.
• Enough Python to build that harness and the tooling around it.
• We are considering candidates who work through Ukrainian FOP.

Nice to have

• Having worked against a closed voice platform, with the frustrations that brings, rather than only your own stack.
• Telephony background: SIP , WebRTC, Asterisk, Twilio or similar. Useful for knowing why calls sound the way they do, though you would not be running any of it.
• Work with health data, or any other field where getting it wrong has consequences.
• German, though we're not counting on it.

You don't need

• To speak German.
• To have built streaming audio infrastructure, a turn-taking system or a speech pipeline. The platform does that, and rebuilding it isn't on the table.
• To know PHP or anything about our existing backend. Another team builds the endpoints you'd call.
• To have trained a model from scratch.

Are you energized by walking into a client’s operation, finding the workflow that’s actually holding them back, and rebuilding it so AI handles the parts that don’t need a person? At Sigma Software, we don’t just add AI on top of existing processes  we redesign how the work gets done, with AI built into the operating logic from day one.

We’re looking for a Forward AI Deployment Engineer to own that redesign end to end: embedded with the client, hands-on with whatever automation the workflow actually needs, and accountable for turning a real operational problem into something running in production not a demo, not a pilot. Each engagement is built to leave something behind for the next one patterns, infrastructure, governance that already held up in production  so the second engagement doesn’t start from zero, and the tenth looks nothing like the first.

Requirements

• Proven experience taking AI systems from prototype to production inside a real operating environment, not just in a lab or sandbox.
• You treat LLM/RAG architectures, agentic systems, cloud platforms, and data pipelines as tools for redesigning how work gets done, not as the deliverable — and you’re strong and current enough with all of them to actually use them that way.
• Comfort mapping decision logic that’s partly undocumented, and judgment about where automation should stop and human accountability should start.

• Excellent communication and stakeholder management — able to advise a client directly and escalate without being asked.

   

  Responsibilities

   

• Embed directly with the client’s operational team to find the workflow that’s actually costing them time, quality, or money before recommending any tooling.
• Own the redesign end to end: rework the decision flow and handoffs around AI, then take the resulting system into production yourself.
• Translate a vague, cross-functional business problem into a concrete technical plan the client’s team can actually execute against.
• Prototype fast enough to show the client what’s possible, then build only the parts of that prototype the redesigned workflow actually needs.
• Where the redesign calls for it, build the agentic systems, RAG pipelines, or custom data pipelines that make it real never a deliverable in itself, always because the workflow needs it running in production.
• Manage stakeholder alignment across the client’s org, escalating early and requesting support before a blocker becomes a delay.
• Build up the client’s own team alongside the system pair with their people, explain the reasoning behind each decision, and leave them able to run and improve what you built after you’ve moved to the next engagement.
• Build the feedback loop from day one  telemetry, evaluation pipelines, human-in-the-loop review — so the system keeps improving after you’ve moved on.

Project description

We are looking for two Senior AI Engineers to join the project, where the team is building an AI platform with agentic capabilities and knowledge-graph-based solutions.
The team currently consists of three engineers and two Project Managers. The engineers will join as hands-on contributors and work closely with the existing team to design, build, and evolve the platform.

Requirements
- Strong professional experience in AI/ML engineering or software engineering with a significant focus on AI.
- Proven hands-on experience building agentic systems, AI agents, or multi-step AI workflows.
- Strong understanding of how agentic systems work beyond simply using existing AI tools or APIs.
- Deep hands-on experience with AWS, including designing and implementing cloud-based solutions.
- Experience with AWS Bedrock, preferably with Claude models.
- Experience working with OpenAI models and APIs.
- Strong programming skills in Python or another relevant language.
- Experience designing and integrating APIs and production-grade services.
- Practical experience with knowledge graphs, graph databases, or graph-based reasoning.
- Strong problem-solving and communication skills.
- Ability to work independently in a hands-on engineering role.

Nice to Have
- Experience with AWS Neptune.
- Experience with Neo4j.
- Experience designing knowledge graph architectures.
- Experience with graph query languages such as Gremlin or Cypher.
- Experience with AI agent frameworks or orchestration tools.
- Experience with RAG, tool use, function calling, planning, memory, and agent evaluation.
- Experience deploying and operating AI systems in production.

Responsibilities
- Design and implement agentic AI systems and workflows.
- Develop production-ready AI platform components using large language models.
- Work with knowledge graphs and graph-based data structures.
- Build integrations with AWS AI and cloud services.
- Contribute to the architecture and implementation of scalable AI solutions.
- Develop, test, debug, and optimize hands-on solutions.
- Collaborate with engineers and Project Managers to deliver project objectives.
- Investigate new approaches and technologies relevant to agentic AI and knowledge graphs.

Team: Three engineers and two Project Managers

We are looking for an AI Solutions Engineer to join our team! The AI Solutions Engineer designs, builds, and operates production-ready AI solutions for IT Operations, IS Operations, and Governance. Working with the AI Enablement Lead and operational experts, the role turns prioritized use cases into secure, maintainable, and reusable AI capabilities.

Responsibilities:

• Design, build and deploy AI agents and AI-enabled solutions.
• Implement Retrieval-Augmented Generation, prompt workflows and process automations.
• Integrate AI solutions with existing applications, knowledge sources and enterprise platforms.
• Develop reusable technical components, integration patterns and implementation standards.
• Work closely with operational experts to turn use cases into production-ready solutions.
• Ensure that solutions are secure, maintainable, scalable and aligned with the wider client's AI technology stack.
• Monitor, support, and continuously improve productive AI solutions.
• Instrument solutions to measure the productivity improvements achieved.

Requirements: 

• Hands-on experience developing and deploying AI solutions in productive environments.
• Strong knowledge of AI agents, Retrieval-Augmented Generation, and prompt engineering.
• Experience with Azure AI, Azure OpenAI, Microsoft Copilot Studio or comparable platforms.
• Strong development skills in Python and experience with REST APIs.
• Experience integrating AI solutions with Microsoft 365 and enterprise applications.
• Knowledge of workflow and automation platforms.
• Experience with GitHub, testing, monitoring, and technical documentation.
• Good understanding of AI security, access management, logging and lifecycle management.

🚀 We are looking for a Middle AI Engineer to build scalable AI-powered applications, developer tools, and workflow automation solutions that improve engineering productivity and deliver measurable business value.

This role is ideal for a strong Python engineer with hands-on experience building and deploying LLM-based applications. You will work on solutions involving RAG, AI agents, model integrations, backend services, and AI evaluation.

🎯 Responsibilities

- Design, build, and deploy LLM-powered applications from prototype to production.

- Develop AI agents, tool-using workflows, and multi-step automation solutions.

- Build and improve RAG pipelines using internal and third-party data sources.

- Integrate commercial and open-weight models such as OpenAI, Anthropic Claude, Google Gemini, and Llama-based models.

- Develop AI-powered capabilities such as code assistance, document generation, summarisation, knowledge search, and workflow automation.

- Build backend services, APIs, integrations, and data flows using Python.

- Implement embeddings, vector search, reranking, structured outputs, and tool calling.

- Evaluate AI outputs and improve relevance, accuracy, reliability, latency, and cost efficiency.

- Add logging, monitoring, error handling, fallback mechanisms, and output validation to AI systems.

- Deploy and maintain AI services in cloud environments.

- Collaborate with product, engineering, and business teams to deliver production-ready AI solutions.

🧩 Requirements

- 3+ years of commercial Python development experience.

- Hands-on experience building and deploying LLM-powered applications.

- Practical experience with at least one of the following:

  - RAG systems;

  - AI agents;

  - multi-step LLM workflows.

- Experience developing backend systems, REST APIs, integrations, and data flows.

- Experience with FastAPI, Django, Flask, or another Python backend framework.

- Hands-on experience with LangChain, LangGraph, LlamaIndex, or a similar AI orchestration framework.

- Experience working with OpenAI, Anthropic, Gemini, Llama-based models, or similar technologies.

- Practical understanding of prompt design, structured outputs, tool calling, and output validation.

- Experience with embeddings, vector search, and at least one vector database.

- Understanding of RAG concepts such as chunking, retrieval, reranking, and response generation.

- Experience evaluating and improving LLM output quality.

- Experience with Docker, Git, automated testing, and CI/CD workflows.

- Experience deploying applications to AWS, Azure, GCP, or another cloud platform.

- Understanding of common production AI challenges, including hallucinations, latency, rate limits, reliability, and cost control.

- English level: Upper-Intermediate or higher.
 

🧩 Nice to Have

- Experience with multi-agent systems.

- Experience building custom tools for AI agents.

- Familiarity with MCP servers, clients, or MCP-based integrations.

- Experience with LLM evaluation and observability tools such as LangSmith, Langfuse, Arize Phoenix, or similar.

- Experience with advanced retrieval techniques, hybrid search, or retrieval evaluation.

- Experience working with open-weight models and model-serving solutions.

- Familiarity with AI safety, guardrails, prompt injection protection, and responsible AI practices.

- Experience with Kubernetes or scalable AI service deployment.

- Experience building AI-powered developer tools.

🧩You'll have an opportunity to:

Build production-ready AI applications used by real users.

Work with modern LLM technologies, AI agents, and RAG systems.

Collaborate with experienced international engineering teams.

Gain hands-on experience with rapidly evolving AI frameworks and cloud platforms.

Solve challenging engineering problems and contribute to scalable AI solutions.

Continuously grow your expertise in Generative AI and modern software engineering

About Faro

Faro is an AI memory layer that allows agentic systems to recall what it learned in the past. Faro currently outperforms industry leader Mem0 on the LoCoMo benchmark (94.1% vs 91.1%). Faro is the memory infrastructure that makes every AI agent more engaging and accurate to work with.

The Role

As an ML/AI researcher at Syntra, you will own the research geared at improving the Faro engine across areas such as: retrieval strategies, memory extraction quality, temporal reasoning, and the evaluation methodology.

You will design benchmarks, build the datasets and LLM judges that score them, run controlled experiments, and turn your research into production ready features. You will play a key role in keeping Faro ahead of its competitors and helping shape the overall product direction.

What you will Work On

• Benchmark datasets, ground truth, and evaluation harnesses
• LLM-judge scoring pipelines with confound controls
• Retrieval, chunking, and memory-extraction experiments
• Accuracy and latency improvements to the core recall engine
• Written recommendations backed by powered, reproducible measurements
• Research code that merges to main in small, production-safe PRs

Must-Haves

• 3+ years of applied ML/NLP research or equivalent industry R&D
• Strong Python capabilities
• LLM pipelines: prompt design, structured extraction, LLM-as-judge evaluation
• Embeddings + vector search (Qdrant, pgvector, Pinecone, or similar)
• Benchmark design: dataset construction, ground truth, statistically powered comparisons
• Engineering discipline: pytest, CI, code review, small reviewable PRs
• Self-starter or experienced with "startup pace"

Nice-to-Haves

• Memory-systems or RAG research (contextual retrieval, temporal reasoning, consolidation)
• Multi-provider LLM experience (OpenAI, AWS Bedrock, Cohere embeddings)
• Agent framework experience (LangChain, LlamaIndex, CrewAI, AutoGen)
• Publications or open-source evaluation and benchmark contributions
• Early-stage startup experience (Series A or earlier)

We will reach out and hold interviews on the week of September 7th, 2026. Thank you.

Our client is the leader in the Early Childhood Education (ECE) sector, providing the most widely used developmental assessment and curriculum system in the US. 

They are early in shaping how AI changes the way they build and operate software. They have active initiatives in AI tooling cost-management and agentic development workflows, but their direction is still forming and will keep evolving as the field does. 

They are not looking for someone to execute a fixed plan, they are looking for an experienced practitioner who can validate the direction we are setting, bring expertise we do not have in-house, stay ahead of what is emerging, and help us adjust and grow as the landscape shifts. This person is hands-on enough to build and prove things directly, and senior enough that we trust their judgment on where to place bets.

What you will do

• Act as a hands-on technical partner across AI engineering initiatives, currently spanning tooling cost management, model routing and selection, and agentic development workflows.
• Pressure-test the approaches they are considering, tell them where they are wrong or where a better path exists, and back it with evidence rather than opinion.
• Bring outside expertise and patterns from how other organizations are solving these problems, so they are not learning everything the expensive way.
• Track what is emerging across models, tooling, frameworks, and techniques, and translate it into concrete recommendations on what they should adopt, defer, or ignore.
• Build and run proofs of concept to validate ideas quickly, then help them decide what to operationalize.
• Help mature their agentic development workflow so engineering teams can adopt it, including model routing and token efficiency.
• Partner with Infrastructure and Security so that what they build fits their architecture and meets their data privacy obligations.
• Help them build internal capability, so the expertise compounds with their team and does not leave when the engagement ends.

Required background

• 5+ years in software, platform, or ML engineering, with recent, direct experience building and operating LLM-backed systems in production.
• Demonstrated breadth across the current AI engineering stack: models and their tradeoffs, inference cost dynamics, gateways and routing, agentic workflows, and the AI coding tool landscape.
• A track record of forming a point of view on fast-moving technical decisions and being right often enough to be trusted with them.
• Strong hands-on engineering ability. This person builds, benchmarks, and ships, not just advises.
• Comfort operating with ambiguity and early-stage direction; able to bring structure rather than wait for it.

• Cloud and infrastructure fluency (AWS preferred, including Bedrock).

   

Strongly preferred

• Experience helping an organization adopt AI-first engineering practices, not just individual tool use.
• Hands-on model evaluation and routing design, and exposure to AI cost governance.
• Experience in regulated data environments. 
• A habit of staying current through real practice (building, writing, contributing) rather than headlines.

What this role is not

• Not a fixed-scope implementation seat. The work will change as their direction evolves, and they want someone who helps drive that change.
• Not a data science or model training role. The focus is applied AI engineering, integration, and enablement.
• Not a manager. Individual contributor, hands-on, with influence earned through expertise. 

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

Шукаємо senior-розробника на новий проєкт, над яким уже ведеться робота — потрібно підхопити наявну кодову базу й розвивати продукт далі.
 

Нам потрібна людина з сильними скілами в AI-розробці, яка вміє інтегрувати моделі, працювати з ембедингами й векторним пошуком і будувати рішення, які потім добре відпрацьовують на масштабі.
 

Стек:
Next.js 14 (App Router) · React 18 · TypeScript · PostgreSQL + pgvector · інтеграції з LLM (OpenAI / Anthropic)
 

Очікуємо:

• 4+ роки комерційної розробки
• Впевнений TypeScript + React / Next.js (App Router обовʼязково)
• Реальний досвід інтеграції LLM / AI API у продукти
• Розуміння prompt engineering, ембедингів, RAG
• PostgreSQL; pgvector чи інші векторні рішення 
• Англійська рівня B2–C1
• Самостійність: треба людина, яка ініціативить, а не чекає прямих тасок
   

Буде плюсом: досвід запуску AI-продуктів з нуля 
 

Як відгукнутись: резюме + пару слів про себе + приклади того, що будували з AI 
(Лінки на GitHub / проєкти вітаються) 

Шукаємо CTO на новий AI-продукт, який уже в розробці - потрібна людина, яка підхопить наявну кодову базу, вибудує технічну стратегію й поведе продукт далі.
 

Нам потрібен технічний лідер із сильною практичною експертизою в AI-розробці: людина, яка не лише розуміє архітектуру, а й сама вміє інтегрувати моделі, працювати з ембедингами й векторним пошуком, будувати рішення, що добре відпрацьовують на масштабі.
 

Стек: Next.js 14 (App Router) · React 18 · TypeScript · PostgreSQL + pgvector · інтеграції з LLM (OpenAI / Anthropic)
 

Очікуємо:

• 4+ роки комерційної розробки, з них досвід у ролі tech lead або вище
• Впевнений TypeScript + React / Next.js (App Router обов'язково)
• Реальний досвід інтеграції LLM / AI API у продукти
• Розуміння prompt engineering, ембедингів, RAG
• PostgreSQL; pgvector чи інші векторні рішення
• Англійська рівня B2–C1
• Самостійність і власна ініціатива - ця роль передбачає, що ти формуєш напрям, а не чекаєш тасок
   

Буде плюсом: досвід запуску AI-продуктів з нуля, досвід побудови команди або технічних процесів
 

Як відгукнутись: резюме + пару слів про себе + приклади того, що будували з AI. Лінки на GitHub / проєкти вітаються.

Шукаємо Senior AI Engineer для розробки AI-рішень у напрямку voice та conversational AI.

Головний критерій — практичний досвід створення та запуску voice-ботів у production. Важливо розуміти повний цикл роботи голосового бота: телефонія, ASR, LLM, TTS, real-time обробка та інтеграція з внутрішніми системами.

Що потрібно робити:

• Розробляти voice-ботів для вхідних і вихідних дзвінків.
• Інтегрувати AI з телефонією, CRM, ERP та API.
• Налаштовувати діалогову логіку відповідно до бізнес-процесів.
• Працювати з ASR, TTS, LLM та streaming-рішеннями.
• Оптимізувати швидкість відповіді та стабільність voice-ботів.
• Створювати AI-чати та RAG-системи.
• Аналізувати дзвінки: транскрипція, контроль якості, дотримання скриптів.
• Працювати разом із backend- та DevOps-командами.

Що для нас важливо:

• Від 3 років комерційного досвіду в розробці.
• Реальний досвід запуску voice-ботів у production.
• Розуміння архітектури ASR → LLM → TTS.
• Досвід роботи з OpenAI, Claude, LLaMA або іншими LLM.
• Впевнене володіння Python: FastAPI, async, API integrations.
• Досвід із PostgreSQL та RabbitMQ або аналогами.
• Розуміння мікросервісної архітектури та real-time систем.
• Досвід створення RAG-систем.

Буде плюсом:

• Досвід у call-center, fintech або collections.
• Робота з Asterisk, WebRTC та streaming.
• Docker / Kubernetes.
• Досвід аналізу та оцінювання якості дзвінків.

Ми пропонуємо:

• Заробітну плату до $6000 залежно від досвіду.
• Повністю віддалену роботу.
• Можливість впливати на архітектуру продукту.
• Роботу над реальними AI-рішеннями для бізнесу.
• Професійний розвиток у voice AI та високонавантажених системах.

Про підрозділ

Центр спеціальних операцій «А» Служби Безпеки України – підрозділ Сил Оборони України, який проводить унікальні спеціальні заходи, що змінюють хід війни. Якщо ти вмотивований і впевнений у своїх силах – проходь відбір, долучайся до команди та давай разом напишемо історію Перемоги!

Ми — інтелектуально-аналітичний напрям підрозділу: працюємо з великими масивами даних у високочутливому контексті, підтримуючи операційні рішення, де критичні точність і швидкість аналізу. Частина деталей наших задач залишається непублічною.
 

Про роль

Ми шукаємо Senior AI Engineer, який здатен не просто експериментувати з моделями, а вбудовувати AI-рішення у реальні продуктові та операційні процеси. Роль передбачає роботу в умовах невизначеності, самостійне проєктування архітектури рішень і відповідальність за їх стабільну роботу в проді.
 

Основні обов'язки

• Проєктування та впровадження AI-систем на базі LLM для роботи зі складними масивами даних.
• Побудова pipeline'ів для обробки, збагачення та структурування текстової інформації (NLP, LLM, RAG).
• Інтеграція AI-рішень із внутрішніми системами та джерелами даних.
• Розробка та оптимізація промптів, агентних сценаріїв і автоматизованих аналітичних процесів.
• Виведення рішень у продакшн: моніторинг якості, продуктивності та стабільності роботи моделей.
• Виявлення неочевидних зв'язків і патернів, що мають вплив на безпеку та ефективність операцій.
• Участь у формуванні та розвитку архітектури аналітичних рішень.
   

Вимоги

• 5+ років практичного досвіду розробки AI/ML-рішень.
• Впевнене володіння Python, досвід роботи з LLM API (OpenAI, Anthropic тощо) та фреймворками для побудови LLM-застосунків.
• Здатність самостійно формулювати задачі там, де немає готових рішень.
• Розуміння принципів роботи мовних моделей (LLM) та досвід їх прикладного використання.
• Аналітичне мислення, системний підхід, відповідальність.
   

Умови

• Формат: мобілізація до кінця воєнного стану або служба за контрактом
• Локація: Київ/Київська область
• Забезпечення всім необхідним спорядженням, обладнанням і ресурсами для виконання завдань
• Задачі з прикладним впливом, у сильній команді професіоналів
• Навчання та постійний професійний розвиток
• Середовище, де важливий результат, а не формальності

З вас — системне мислення та відповідальність.
З нас — складні, змістовні й по-справжньому важливі задачі.