Jobs ML / AI

This is not a coding role. This is a systems-of-systems role. 

If you build things with AI, this posting isn't for you. 

If you build the systems that govern how AI builds things — keep reading.

What This Role Is

LifeChef is a HIPAA-compliant healthcare platform delivering Medically Tailored Meals. Our engineering team builds on a methodology called Enso — a spec-driven, AI-augmented development framework where every feature starts with a specification, gets built with AI assistance, and is validated against defined criteria.

Enso v1 is linear: spec → generate → validate → ship. It works. But it's hitting scaling limits — the document graph grows with every feature, validation complexity compounds, and the process requires increasing human oversight to maintain quality. This is a familiar problem in any AI-assisted workflow that outgrows its initial architecture.

We need someone to take Enso from v1 to v2: a true agentic system where specialized AI agents handle research, specification validation, code generation oversight, quality assurance, and deviation detection — with structured autonomy, clear escalation paths, and human-in-the-loop at defined decision points, not everywhere.

You would partner directly with the CTO (who designed the current methodology and has strong opinions about where it needs to go) to architect, build, and maintain this system. The first 30 days are heavy collaboration — translating vision into working architecture. After that, you own the evolution of the process while the CTO focuses on product and team.

What You'd Actually Build and Maintain

• Agent topology for the development lifecycle: research agents, spec validation agents, code review agents, compliance checking agents, red-team/adversarial agents — each with defined boundaries, confidence thresholds, and escalation triggers
• Spec-to-code reconciliation system: agents that validate whether implemented code actually satisfies the specification, flagging deviations and ambiguities rather than silently passing
• Multi-model orchestration strategy: routing different tasks to different models based on cost, capability, and risk (expensive models for reasoning, cheap models for classification, open-source where it fits)
• Structured output validation: ensuring agent outputs are parseable, auditable, and actionable — not just "LLM said it's fine"
• Governance and escalation framework: what agents decide autonomously vs. what requires human sign-off, circuit breakers for low-confidence situations, audit logging for compliance
• Evolution engine: the system should improve itself — tracking where agents fail, where human overrides happen, and feeding that back into prompt refinement and architecture changes
• Integration with existing CI/CD: the agentic layer hooks into GitHub Actions, automated testing (Vitest), PHI scanning, secret detection, and compliance validation already in the pipeline

This is infrastructure that the development team operates within. You're not shipping product features — you're shipping the machine that makes product development faster, safer, and more consistent.

What We Need From You

Hard requirements:

• You have designed or built at least one agentic system where AI agents operate with structured autonomy — multi-step workflows with validation, error handling, and escalation. Not just API wrappers. Not just chatbots.
• You understand agent topology as a design problem: which agents need to exist, what their boundaries are, how they communicate, how you prevent cascading failures.
• You have hands-on experience with LLM orchestration frameworks (LangChain, CrewAI, AutoGen, custom implementations — the specific tool matters less than the depth of understanding).
• You think in terms of eval frameworks and validation: how do you know if an agent's output is good? How do you measure drift? How do you build confidence scoring that's actually useful?
• You understand multi-model strategies: when to use which model, cost/capability tradeoffs, how to tier your model usage so you're not burning expensive tokens on classification tasks.
• You can work in both Python and TypeScript, or at minimum are comfortable in one and willing to operate in the other. Our product stack is TypeScript-first but agentic tooling often lives in Python.
• Strong written communication. This role involves heavy async documentation of architectural decisions, process design, and system behavior.

What sets you apart:

• Experience with Model Context Protocol (MCP) — building custom MCP servers, tool-use architectures
• Claude Code, Cursor, or similar AI-native development tools as part of your own workflow (you should be using what you're building systems around)
• Experience in healthcare, fintech, or regulated environments where AI outputs must be auditable and compliant
• Understanding of prompt engineering at the systems level — not "write a good prompt" but "design a prompt architecture that's maintainable, versionable, and testable"
• You've thought seriously about adversarial validation — red-team agents, cross-model verification, structured disagreement as a quality signal
• You have opinions about where agentic AI is overhyped and where it genuinely works, and you can defend those opinions with specifics

What we explicitly don't require:

• A portfolio of 50 agentic projects. The field is young. One deep project with real orchestration depth beats ten shallow ones.
• Product development skills. You won't be building UI or shipping user-facing features.
• Healthcare domain expertise. We'll teach you the domain. You teach us the orchestration.

How We Evaluate

1. Screening questions (in your application) — We're looking for specificity and honesty, not keyword density.
2. Architecture conversation with CTO (90 min) — We'll describe our current system, its limitations, and where we want to go. You'll tell us how you'd approach it. This is a two-way design conversation, not a quiz.
3. Small design exercise (async, take-home) — Given a simplified version of our spec validation problem, design an agent topology. We're evaluating how you think about boundaries, failure modes, and escalation — not production-ready code.
4. Trial sprint (60–90 days) — Heavy pairing with CTO in the first 30 days, increasing autonomy after. Defined milestones at 30, 60, and 90 days.

The Team and Context

Small, senior engineering team, primarily Ukraine-based. The CTO (Jay) designed the Enso methodology, has 20+ years of engineering experience across healthcare, threat intelligence, and enterprise platforms, and has built the governance architecture (constitutions, spec-gates, validation layers) that you'd be evolving. He's technical, direct, and has strong opinions — but he's hiring you because he needs a partner in this, not an executor.

The product stack is Next.js 16, React 19, TypeScript, PostgreSQL, AWS serverless (Lambda, CDK, Amplify Gen 2), with Vitest testing, Pino structured logging, and HIPAA compliance enforced in CI. You don't need to know this stack deeply, but you need to understand it well enough to build agentic tooling that integrates with it.

We're building toward autonomous agentic systems for clinical operations — this development process work is the foundation that makes that possible. If the idea of designing the system that governs how an engineering team builds software excites you, this is the role.

How to Apply

Reply with:

• The most complex agentic or LLM orchestration system you've built — what it did, how it was structured, what you learned
• Your honest take on where agentic AI is right now and what's actually hard
• What interests you about building process infrastructure vs. product features

Generic responses get skipped. We know the field is young — we'd rather hear about one deep experience than a list of tools you've touched.

WHO WE ARE

SouthRivers Data, we're an engineering-led company - built by engineers, run by engineers. That means no explaining why tech debt matters, no translating "we need refactoring time" into business speak, and no decisions made by people who've never written a line of code. We get it, because we've been there.

We believe that a great engineer is defined by two things equally: technical mastery and the ability to work with people. One without the other isn't enough for us - and probably isn't enough for you either.

REQUIREMENTS

• Experience in Python programming (at least 3 years of practical experience in Python programming)
• Practical experience with GenAI and agentic AI in Python (ideally using
• pydantic-ai as a framework), at least one year of practical experience, more is an advantage)
• Hands-on experience in developing complex end-to-end (Gen) AI applications (not necessarily alone, but as part of a cross-functional team), but in the role of Solution Engineer or AI Engineer
• Team player who can also work independently and proactively take on and execute tasks

• Excellent written and spoken English skills

   

NICE-TO-HAVE

• • Theoretical knowledge of the different concepts in the field of agent-based AI (models, tools, inferences, memory and related approaches such as RAG, protocols such as MCP)
• Experience in data science and machine learning
• Experience setting up CI/CD pipelines and steps (preferably Github Actions)
• Experience working in the Azure cloud
• Experience with Kubernetes and setting up deployments, jobs, etc.

🌎Akvelon is an American company with offices in Seattle, Mexico, Ukraine, Poland, and Serbia. Our company is an official vendor of Microsoft and Google. Our clients also include Amazon, Evernote, Intel, HP, Reddit, Pinterest, AT&T, T-Mobile, Starbucks, and LinkedIn. 

To work with Akvelon means to be connected with the best and brightest engineering teams from around the globe and working with an actual technology stack building Enterprise, CRM, LOB, Cloud, AI and Machine Learning, Cross-Platform, Mobile, and other types of applications customized to client’s needs and processes.

About the project:
We’re looking for a Senior AI Engineer to join a digital transformation initiative at a leading global private equity firm. The project focuses on building AI-driven solutions — including document AI and LLM-based systems — to automate insights extraction from complex financial materials and support investment decision-making, valuation, and due diligence workflows.

About the role:
You’ll design and build end-to-end AI systems: LLM pipelines, RAG architectures, and automated evaluation frameworks. Responsibilities include fine-tuning models, designing accuracy and hallucination evaluation metrics, and integrating AI solutions into core business processes. The role combines deep ML/LLM expertise with the ability to translate model outputs into actionable business insights.

Requirements:

Must have:

• Strong Python and ML ecosystem experience (PyTorch / TensorFlow / Hugging Face)
• Ownership of agent-based systems
• Hands-on experience with LLM systems (fine-tuning, evaluation)
• Solid understanding of RAG architectures
• Cloud platforms and Docker

Nice to have:

• Knowledge of finance or private equity domain
• MLOps experience
• Business analytics understanding
• Background in finance, consulting, or technology projects
• Experience with production multi-agent systems

Benefits:

• Paid vacation and sick leave
• Official state holidays — 9-11 public holidays per year
• Professional growth through challenging projects and the opportunity to master new technologies
• Flexible working schedule: 8 hours per day, 40 hours per week
• Personal Career Development Plan (CDP)
• Employee support programs (discounts, healthcare, legal assistance)
• Paid external training, conferences, and professional certifications aligned with business goals
• Internal workshops, seminars, and English classes
• Corporate library with paper and e-books

If this role sounds like a match, apply now and send us your CV.

We’re on the hunt for a talented AI/ML Engineer to design, implement, and optimize cutting-edge AI/ML solutions. If you thrive on solving complex problems and creating production-ready models, we’d love to hear from you!

.  What You’ll Do:

• Develop and fine-tune AI/ML models for performance and scalability.
• Enhance production models, debug issues, and ensure reliability.
• Work with cross-functional teams to shape future AI/ML innovations.
• Explore emerging AI/ML technologies and recommend tools/frameworks.
• Build robust pipelines using Python, SQL, Docker, and more.

.  What We’re Looking For:

• Strong experience in AI/ML, data science, or data engineering.
• Expertise in Python, TensorFlow, Keras, Scikit-learn, and Generative AI.
• Familiarity with Docker, Streamlit, and data tools like Pandas and SQL.
• Strong communication skills and the ability to work independently.

• English: Upper-intermediate or higher.

   

  What We Offer:

✔️ 18 vacation days + 12 sick leaves annually.
✔️ A high-tech workplace (MacBook Pro + Dell monitor).
✔️ Exciting projects with cutting-edge technologies.
✔️ Flexible work hours and competitive salary.
 

Ready to shape the future of AI/ML with us? 🚀 Apply now and be part of something extraordinary!

About Us

Our client is MilTech startup building an on-premises platform for automated annotation of UAV video and training computer vision models for autonomous flight.

We have a strategic partnership with one of the largest Ukrainian UAV manufacturers and access to what is likely the largest privately owned UAV video dataset in the world.

The company is at a very early stage — the funding is secured, the technical vision exists, and the data is available. Our focus now is building the core engineering team to turn this platform into reality.

The Role

We are looking for a Lead Computer Vision / Machine Learning Engineer to join as a founding member of the engineering team.

This role is highly hands-on and strategic. You will define the ML architecture, select the tools and frameworks, and build the machine learning pipeline from the ground up. As the platform grows, you will also build and lead a team of ML engineers.

While the high-level technical direction is defined — including petabyte-scale video processing, active learning pipelines, and edge deployment on NVIDIA Jetson devices — the implementation details will be driven by your expertise and technical judgement.

Responsibilities ML Architecture & Technical Direction

• Design and implement the end-to-end ML pipeline, from raw video ingestion to model deployment on UAV hardware
• Evaluate and select appropriate frameworks and infrastructure tools
• Build systems for experiment tracking, model versioning, and reproducible training
• Establish the training infrastructure and ML development workflow
• Make strategic build vs. integrate decisions across the ML stack

Data Pipeline & Curation

• Design video preprocessing pipelines including:
   
  • clip segmentation
  • frame extraction
  • scene detection
  • embedding generation
• Develop data curation workflows to extract high-quality training datasets from large-scale raw video
• Define the annotation ontology and labeling standards for detection, classification, segmentation, and multi-object tracking

Model Development

• Train and improve computer vision models for:
   
  • object detection
  • scene classification
  • object tracking
  • segmentation
• Implement distributed multi-GPU training for large-scale experiments
• Design and implement active learning pipelines including:
   
  • automated labeling
  • confidence-based routing
  • human review workflows
  • retraining triggers
• Define model evaluation metrics and promotion criteria

Edge Deployment

• Optimize models for real-time inference on NVIDIA Jetson hardware
• Implement model optimization techniques including:
  • ONNX conversion
  • TensorRT acceleration
  • INT8 quantization
• Design the model delivery pipeline from training registry to deployed UAV systems

Team Building

• Hire and mentor ML engineers as the team grows
• Establish engineering practices including:
  • code review
  • testing standards
  • documentation
• Own and communicate the ML technical roadmap

RequirementsMust Have

• 5+ years of experience in computer vision and deep learning, including production deployments
• Strong proficiency with PyTorch, including distributed training
• Experience building object detection and tracking pipelines (YOLO, RT-DETR, Detectron2, or similar)
• Experience optimizing models for edge or mobile deployment (TensorRT, ONNX, SNPE, or similar)
• Experience building ML infrastructure and training pipelines
• Strong Python skills and ability to write production-quality code
• Experience leading or mentoring engineers

Nice to Have

• Experience with UAV, drone, robotics, or autonomous systems
• Experience deploying models on NVIDIA Jetson
• Experience with high-performance video processing pipelines (DeepStream or custom)
• Experience in defense or MilTech environments
• Experience defining ML architecture from scratch in early-stage startups
• C++ experience for performance-critical components

What We Offer

• Founding engineering role with direct influence on company technology
• Ownership of the entire ML pipeline from raw data to deployed models
• Access to one of the largest privately owned UAV video datasets in the world
• Dedicated on-prem GPU infrastructure
• Open-source-first engineering approach
• Opportunity to build and lead a machine learning team
• Competitive salary
• Stock options

About Us

Our client is MilTech startup building an on-premises platform for automated annotation of UAV video and training computer vision models for autonomous flight.

We have a strategic partnership with one of the largest Ukrainian UAV manufacturers and access to what is likely the largest privately owned UAV video dataset in the world.

The company is at a very early stage — the funding is secured, the technical vision exists, and the data is available. Our focus now is building the core engineering team to turn this platform into reality.

The Role

We are looking for aComputer Vision / Machine Learning Engineer.

We are looking for  CV/ML Engineer to join as one of the first engineers on the team. You will work closely with the technical lead to build the ML pipeline from the ground up — implementing core components across data processing, model training, and deployment.

This is an early-stage startup, so the tech stack and processes are still being shaped. You will have real input into the tools and approaches we adopt, and your work will directly define how the platform operates. We are looking for someone who is hands-on, curious, and comfortable building things that don’t exist yet.
 

What You Will Work OnData Pipeline & Preprocessing

• Build video preprocessing pipelines: clip segmentation, frame extraction, scene detection, quality filtering
• Implement embedding extraction and data curation workflows for selecting diverse training subsets from a massive video corpus
• Prepare and export datasets in standard formats for training and annotation
   

Model Training & Experimentation

• Train and evaluate computer vision models for object detection, classification, tracking, and segmentation
• Run experiments, track results, and iterate on model architectures and hyperparameters
• Implement data augmentation strategies for robustness across weather, lighting, and occlusion conditions
• Contribute to distributed multi-GPU training pipelines
   

Active Learning & Annotation Support

• Implement auto-labeling pipelines with confidence-based routing
• Build tooling around the annotation platform (pre-annotation, format conversion, quality
• Maintain automated quality checks for label consistency
   

Edge Deployment

• Export and optimize models for inference on NVIDIA Jetson (ONNX, TensorRT, FP16/INT8quantization)
• Run accuracy and latency benchmarks on edge devices
• Help maintain the model delivery pipeline from training to deployment
   

Requirements Must Have

• 3+ years of hands-on experience in computer vision and deep learning
• Solid PyTorch skills — comfortable writing training loops, custom datasets, and model code
• Experience with at least one detection or segmentation framework (YOLO family, Detectron2, MMDetection, or similar)
• Understanding of model evaluation metrics (mAP, IoU, precision/recall)
• Familiarity with experiment tracking (MLflow, W&B, or similar)
• Python as primary language; clean, well-structured code
• Comfortable working in Linux and containerized environments (Docker)
   

Nice to Have

• Model optimization experience (ONNX export, TensorRT, quantization)
• NVIDIA Jetson or other edge/mobile deployment
• Multi-object tracking (ByteTrack, DeepSORT, or similar)
• Experience with video processing pipelines (FFmpeg, OpenCV, decord)
• Data curation or annotation tools (FiftyOne, CVAT, Label Studio)
• Distributed training (DDP, Ray Train)
• UAV / drone / robotics or MilTech domain experience
   

What We Offer

• Join an early-stage team where your contributions shape the product from day one
• Work across the full ML pipeline — not just one narrow piece
• The largest privately owned UAV video dataset in the world — petabyte-scale, impossible toreplicate
• Dedicated on-prem GPU resources — no cloud queues or budget negotiations for compute
• Open-source-first approach with no vendor lock-in
• Growfast in a small team with direct mentorship from the technical lead

We’re looking for an Senior AI Automation Engineer (Full-time or Part-time) with hands-on experience in building AI-powered automations and agent-based workflows, and operating such systems in production

This role is for someone who can independently take a business task, design an AI-driven automation solution, and deploy it into production
 

What You’ll Do
 

• Design and build complex business automation workflows using N8N and Make, Zapier as an additional tools
• Implement AI-powered logic within automation flows using leading LLMs (OpenAI, Claude, Gemini, etc.)
• Design and configure agent-based scenarios for multi-step reasoning and task execution
• Apply advanced prompt engineering to ensure stable and predictable LLM behavior
• Integrate automation systems with external platforms via APIs
• Deploy and maintain AI automation flows in production environments
• Work directly with stakeholders to clarify tasks and translate business needs into technical solutions
   

Requirements
 

• 2+ years of experience building AI-driven automations and agent-based workflows
• Strong practical experience with N8N, experience with Make and/or Zapier is a plus
• Hands-on experience working with major LLMs (OpenAI, Claude, Gemini, etc.)
• Solid understanding of agent-based system architecture
• Advanced prompt engineering skills
• Strong understanding of system integrations and REST APIs
• Experience deploying and operating AI automation systems in production
• Ability to work autonomously from task clarification to production deployment
• English level B2+
   

Would Be a Plus
 

• Previous experience with RPA platforms
• Programing background with Python or JavaScript
• Experience building AI chatbots for open-ended communications
   

Why Join Us
 

• Architect and implement complex AI-driven automation systems, not just simple integrations
• Deliver production solutions for top international clients across the US, Canada, and UK
• Flexible remote work format (full-time or part-time)
• 20 paid vacation days and 5 paid sick days for full-time position 
   

We hire for an engineering mindset and ownership. If you're ready to move beyond simple prototypes and embed best-in-class engineering practices into AI automation - we’d love to hear from you

Overview
We are looking for a Machine Learning Engineer to design and implement intelligent systems that power personalized digital experiences across connected devices and mobile applications.

This is a hands-on role focused on applied machine learning and data engineering, ideal for someone who thrives at the intersection of ML models, data pipelines, and product integration. You will collaborate closely with backend engineers, data scientists, and product managers to bring ML-driven features into production and continuously optimize them based on real-world usage.

Responsibilities:

• Design, train, and deploy machine learning models for use cases such as personalization, recommendations, and visual analysis;
• Build and maintain scalable, production-grade data pipelines (batch and real-time);
• Collaborate with product and engineering teams to translate product ideas into ML-powered features;
• Implement model observability, monitoring, and retraining workflows;
• Optimize inference performance and model serving in resource-constrained environments (e.g., edge or mobile);
• Contribute to the architecture of a modern ML platform within a cloud-native microservices environment;
• Work with backend teams to expose model outputs via APIs and integrate them into customer-facing experiences;

• Stay up to date with recent ML/AI advancements and evaluate promising methods for future implementation.

   

Requirements:

• 3+ years of experience in applied machine learning or data science;
• Strong knowledge of:
  • Supervised learning, recommendation systems, or computer vision;
  • Python and ML libraries such as TensorFlow, PyTorch, scikit-learn;
  • Building, deploying, and maintaining ML models in production.
• Familiarity with:
  • MLOps tools (e.g., MLflow, TFX, Airflow, Vertex AI);
  • Data pipelines and messaging systems (e.g., Kafka, BigQuery, dbt);
  • Model serving frameworks (e.g., FastAPI, TensorFlow Serving, TorchServe, ONNX);
• Comfortable collaborating with product, engineering, and analytics teams;
• Experience with cloud platforms (GCP, AWS, or Azure) and container-based deployments;
• Bonus: Experience with edge AI, mobile ML, or video/image processing pipelines.

ABOUT COMPANY

Fast-scaling DTC supplement brand focused on lymphatic drainage. We run a high-output creative team and ship winning ad concepts every week.

THE ROLE

You'll take creative briefs from our Creative Strategist and produce polished, performance-ready video ads — fast. You're expected to use AI tools as part of your workflow and deliver without hand-holding.

WHAT YOU'LL DO
— Receive briefs from the Creative Strategist and execute from start to finish

— Edit and produce 10–20 ad concepts per week

— Handle captions, b-roll, visual overlays, and all post-production

— Prepare every deliverable to IEDC structure (Intro, Engagement, Detail, CTA)

— Implement feedback quickly and stay on schedule

WHAT WE'RE LOOKING FOR

— Experience editing performance video ads for DTC or e-commerce brands

— Comfortable using AI tools to speed up your workflow

— Fast, clean output — quality without slow turnarounds

— Self-directed, reliable, and communicates clearly

COMPENSATION

Starting at $2,500/month for 10–20 concepts per week. As your quality and output improve, salary grows up to $5,000/month. Performance bonuses available on top of base.

This is a full-time role — no freelance or side engagements to start.

OUR VALUES

— Hungry — you want to grow

— Speed — we move fast

— Ownership — no chasing you down

— Results Over Activity — output matters, not effort

COMPANY

Arcyn is a freshly funded seed stage startup. We’re creating an AI-powered compliance checker for architectural design — an Autodesk Revit plugin that automatically checks designs against regulations as they're being created. Architects get instant feedback on violations as they design in Revit, instead of waiting weeks for permit reviewers to tell them what's wrong.

We’ve just closed pre-seed round and are building core technical team. We’re now a team of 4 with a track record of building complex technical products, and we're looking for a strong AI engineer who's comfortable testing hypotheses and working across different frameworks. This is a chance to join at the ground floor, shape product architecture and solve genuinely hard AI problems.

----

In this role, you'll architect AI systems from scratch and own the full development cycle, there's plenty of room for experimentation and rapid iteration. We're seeking a highly skilled AI Engineer with strong experience in orchestrating multi-step LLM workflows, automated code generation, and compliance logic formalization.

RESPONSIBILITIES: 
• Design and maintain modular, multi-step AI workflows for compliance automation. 
• Integrate multiple LLM providers (e.g., OpenAI, Anthropic, Perplexity) into a cohesive orchestration pipeline. 
• Build cost-optimized batch processing and intelligent API request strategies. 
• Translate complex regulations into structured, machine-executable logic. 
• Develop and validate automatically generated code to ensure correctness, safety, and performance. 
• Collaborate with domain experts to refine logic interpretation and edge case handling. 

• Maintain high-quality, well-structured Python codebases with clear separation of concerns. 

REQUIREMENTS  
• 5+ years of professional software engineering experience. 
• Strong Python skills, including experience writing production-grade, modular code. 
• Proven track record building multi-step LLM workflows (LangChain, LangGraph, or similar). 
• Experience in structured code generation and validation. 
• Strong debugging skills and comfort working with complex, evolving systems. 

   NICE TO HAVEs: 
   • Experience with compliance automation, technical regulations, or domain-specific rule translation. 
   • Familiarity with Pydantic models or other type-safe data management frameworks. 
   • Background in developer tooling or systems requiring high reliability and reproducibility. 
   • Experience optimizing LLM API usage for both cost and speed. 

WE OFFER:

• B2B set-up via SP account
• Full-time remote role aligned with EEST
• Be part of the team shaping a new AI product at an early stage. 
• Freedom to architect solutions using the best tools for the job. 
• Collaboration with a multidisciplinary, innovation-driven team. 
• Flexible work arrangements and competitive compensation.

About the Role

We are looking for an experienced Voice AI Agents Engineer who will be responsible for building, testing, and improving AI voice agents used in our products and internal tools.

This role focuses specifically on the development of Voice AI agents — systems that can understand speech, process user intent, and respond naturally using modern AI models.

You will work closely with the product and engineering teams to design reliable and scalable voice interactions, experiment with new technologies, and continuously improve the performance of our AI agents.

Responsibilities

• Design, build and maintain Voice AI agents
• Develop pipelines for speech-to-text, LLM processing, and text-to-speech
• Test and improve conversation flows and voice interaction quality
• Integrate voice agents with internal systems, APIs, and business tools
• Monitor performance and optimize response quality, latency, and reliability
• Experiment with new AI models and voice technologies
• Work closely with product managers and developers to ship AI-driven features

Requirements

• 1.5+ years of hands-on experience building Voice AI agents
• Strong experience with LLMs and AI agent architectures
• Experience working with speech-to-text and text-to-speech systems
• Solid programming skills (Python or Node.js)
• Experience integrating AI systems with APIs and external services
• Understanding of conversational AI design and dialogue systems
• Ability to independently design and implement AI-driven solutions

Nice to Have

• Experience with telephony or real-time voice systems
• Experience with frameworks for building AI agents
• Experience with prompt engineering and evaluation pipelines
• Experience with cloud platforms (AWS, GCP, or similar)

What we offer

• Fully remote work format and flexible working hours.
• Competitive salary pegged to USD.
• 24 days of paid vacation (sick leave, national, religious, and company holidays).
• A friendly communication culture and transparent processes.
• Dynamic work environment.
• Opportunities for professional development and career growth.
• No micromanagement — just trust, ownership, and freedom to experiment.

Automat-it is an all-in AWS Premier Partner and Managed Services Provider specializing in the startup ecosystem. With over 800 customers and 500+ AWS certifications, Automat-it brings hands-on expertise in AI, DevOps, and FinOps to empower fast-paced startups to grow, deliver & win. Our customers save significant time-to-market and optimize their cloud performance and costs.

We work across EMEA and the US, fueling innovation and solving complex challenges daily. Join us to grow your skills, shape bold ideas, and help build the future of tech.

We seek an experienced Senior AI Engineer to join our team. You will work hands-on with innovative startups' Data Science and R&D teams and be in charge of various projects. You will develop robust MLOps pipelines, create cutting-edge Generative AI solutions, implement the latest technologies, and ensure the customers’ success by overseeing the project from inception to completion.

📍 Work location - remote from Ukraine

If you are interested in this opportunity, please submit your CV in English.

Responsibilities

• Developing backend Generative AI solutions and POCs, utilizing RAG pipelines, agent-based systems, and LLM-powered services
• Taking existing ML or customer-provided code and adapting it for training, fine-tuning, and production deployment using PyTorch
• Developing and optimizing fine-tuning workflows, model evaluation, benchmarking, and inference optimization
• Provision of ML AWS resources and infrastructure primarily using managed and cloud-native services
• Developing ML-oriented CI/CD pipelines using GitHub Actions, Bitbucket, or similar tools
• Deploying Machine Learning models in production and ensuring they are observable and monitorable
• Collaborating with Solution Architects and DevOps teams on scalable and cost-efficient AI infrastructure
• Learning new skills and technologies quickly to stay up to date with rapidly evolving GenAI tools
• Using and writing Terraform libraries for infrastructure deployment
• Maintaining infrastructures and environments of all types, from dev to production
• Security monitoring and administration

Requirements

• Excellent customer-facing skills to understand and address client needs effectively
• Ability to design and implement cloud solutions and to build GenAI pipelines on AWS
• Experience with one or more large language model frameworks like OpenAI SDK, Amazon Bedrock, LangChain, and LlamaIndex
• Fluency in Python, a good understanding of Linux, and mandatory hands-on production experience with PyTorch
• Ability to understand tools used by data scientists and experience with building Generative AI applications beyond notebooks or demos
• Good understanding of supervised and unsupervised learning, and deep learning concepts
• Fluent written and verbal communication skills in English
• Ability to work hands-on, independently, and deliver under real project constraints
• Bachelor’s or Master’s degree in AI-related fields - an advantage
• Working knowledge of some Vector Databases (OpenSearch, Qdrant, Weaviate, LanceDB, etc.) - an advantage
• Experience with Docker and Kubernetes - an advantage
• Experience with Azure or GCP (DevOps/MLOps) for ML or GenAI workloads - an advantage
• ML certification (AWS ML Specialty or similar) - an advantage

Benefits

• Professional training and certifications covered by the company (AWS, FinOps, Kubernetes, etc.)
• International work environment
• Referral program – enjoy cooperation with your colleagues and get a bonus 
• Company events and social gatherings (happy hours, team events, knowledge sharing, etc.)
• English classes
• Soft skills training

Country-specific benefits will be discussed during the hiring process.

Automat-it is committed to fostering a workplace that promotes equal opportunities for all and believes that a diverse workforce is crucial to our success. Our recruitment decisions are based on your experience and skills, recognising the value you bring to our team.

SoftButler seeks a Middle AI Test Engineer to ensure the quality of LLM-based systems through structured testing approaches. We're an innovation-driven startup valuing ownership and technical depth. In this role, you will focus on testing AI chatbots, evaluating model outputs, and contributing to reliable AI testing practices.

RESPONSIBILITIES:

• Develop and maintain a testing framework based on promptfoo for LLM testing.
• Write tests for AI chatbots: functional and security-focused (Red Teaming, Security, Privacy).
• Configure and maintain evaluation of model responses (LLM-as-a-judge).
• Test multi-step conversations and complex dialogue flows (multi-conversation flow).
• Generate synthetic datasets for various AI testing scenarios.
• Write clear, well-structured code with comments as educational material for students of different levels.

REQUIREMENTS:

• B2 English (for communication and technical documentation).
• Strong understanding of QA principles and test design techniques.
• Experience with JavaScript or Python.
• Basic understanding of LLM behavior (prompting, limitations, response evaluation).
• Analytical thinking and attention to detail.
• Ability to write clean, readable, and well-structured code.

NICE TO HAVE:

• Experience with prompt engineering or tools like promptfoo.
• Basic understanding of AI safety (Red Teaming, prompt injection).
• Experience with conversational AI or chatbots.
• Understanding of NLP basics.

WHAT WE OFFER:

• Ownership: contribute to building AI testing practices from scratch.
• Mentorship: support from experienced engineers and knowledge sharing.
• Career growth: development toward AI QA / AI Quality Engineer roles.
• Flexible work: fully remote or hybrid (Kyiv office).
• Full-time position: long-term collaboration.
• Corporate events: team-building and community activities.

Project Description:
One of the world's largest providers of products and services to the energy industry has a need to develop, support and integrate software system in Oil & Gas domain.
You will be a member of a cross functional team.
Key project stakeholders are open for innovative ideas.
Project is based on SCRUM methodology.
This is a great opportunity to work in an international team, apply and learn modern IT technologies.

Responsibilities:

• Application Security with strong AI/ML security exposure
• Threat modeling and risk assessment for AI systems, APIs, and cloud-native applications
• Protection against prompt injection, adversarial ML, data poisoning, and model abuse
• Secure SDLC and CI/CD integration, including MLOps pipelines
• Hands-on experience with SAST, DAST, SCA, container scanning, and security automation
• Strong collaboration with development and data science teams

Mandatory Skills:

• 5+ years in Application Security, with at least 1–2 years focused on AI/ML security
• Strong secure coding and vulnerability management background (OWASP Top 10)
• Experience with cloud platforms (AWS and/or Azure)
• Strong programming skills in Python or Java
• Familiarity with AI platforms such as Amazon Bedrock, SageMaker, or Spark
• Strong communication skills

Nice-to-Have Skills:

• Good knowledge of Microsoft Active Directory services
• Firewall and security group administration practical experience
• Good understanding of AICPA SOC2 controls and processes
• Thorough knowledge of network security and protocols
• Knowledge of cryptography protocols and hands-on experience in SSL certificates generation
• Penetration testing hands-on experience in Azure and AWS cloud environments and Kubernetes clusters
• Web and desktop applications penetration testing hands-on experience

Languages:

• English: B2 Upper Intermediate

About the Role & Project

We’re looking for a Middle AI Engineer to help build practical AI solutions for real-world business needs.

In this role, you’ll work hands-on with LLMs and GenAI, collaborating with engineers, solution designers, and product teams. You’ll be involved in designing, prototyping, and deploying AI features, with room to grow into more advanced or client-facing responsibilities over time.

Location: Remote / Hybrid (Ukraine / EU), with occasional travel if needed

What You’ll Do

AI Development & Implementation

• Build and integrate AI / GenAI solutions into products
• Work with LLM APIs (OpenAI, Azure OpenAI, etc.)
• Develop and improve RAG pipelines and AI-powered features
• Create PoCs and MVPs to validate ideas

Systems & Integration

• Integrate APIs and third-party services
• Work with databases, including vector databases
• Troubleshoot and optimize existing solutions

Team Collaboration

• Work closely with engineers and solution designers
• Contribute to architecture discussions
• Participate in code reviews

AI-Enhanced Development

• Use tools like Copilot, Cursor, etc. to speed up development
• Apply prompt engineering techniques
• Validate and refine AI-generated outputs

What We Expect

Experience

• 3+ years in software development (Python or similar)
• ~1 year of hands-on experience with AI / LLMs
• Experience building and integrating APIs
• Experience working on real-world projects

Technical Skills

• Python (APIs, async, production-level code)
• LLM basics (prompting, APIs, understanding of RAG)
• REST APIs (GraphQL is a plus)
• SQL + basic knowledge of vector databases
• AWS or Azure (basic level)
• Docker (Kubernetes is a plus)

Nice to Have

• Experience with RAG or AI agents
• AI-related pet projects or open-source contributions
• Some exposure to client communication or presales

Soft Skills

• Clear communication of technical ideas
• Proactive and самостоятельный mindset
• Comfortable working with some ambiguity
• Willingness to learn and grow in AI

Languages

• English: Upper-Intermediate (B2+)
• Ukrainian: required

What We Offer

• Flexible working schedule
• Remote-friendly setup
• Stable employment
• Paid vacation and sick leave
• Growth opportunities within the project