AI Engineer

Kyiv · KyivRemotemid

EngineeringData ScienceMachine Learning EngineerAI EngineerData

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Quick Summary

Key Responsibilities

AI Agent & Chatbot Development Design, build, and deploy conversational agents using Azure AI Foundry, Microsoft Agent Framework, and OpenAI. Develop prompt strategies for context-aware,

Requirements Summary

English level – B2 or higher 5+ years of experience in AI engineering and.NET software development (.NET 10, ASP.NET Core/WebAPI, C#) with frontend experience in React 19 / Next.js 15 / TypeScript,

Technical Tools

EngineeringData ScienceMachine Learning EngineerAI EngineerData

Position Name: AI Engineer
Reports to: DevOps Manager
Location/Type: UA/Remote

Atlas Technica's mission is to shoulder IT management, user support, and cybersecurity for our clients, who are hedge funds and other investment firms. Founded in 2016, we have grown year over year through our uncompromising focus on service.

We value ownership, execution, growth, intelligence, and camaraderie. We are looking for people who share our Core Values, thrive, and contribute to this environment while putting the customer first. At Atlas Technica, we offer a competitive salary, comprehensive benefits, and great perks to our global Team. We strive to maintain a professional yet friendly environment while promoting professional and career development for our Team Members. Join Atlas Technica now!

As an AI engineer, you will design and deploy intelligent chatbots using Foundry Workflows, Microsoft Agent Framework, OpenAI; build and operate multi-agent orchestration pipelines with Azure AI Foundry and Model Context Protocol (MCP); integrate AI with enterprise systems via RESTful APIs, ensuring compliance with security and privacy standards. Stay current with LLM advancements, collaborate across teams, and mentor junior engineers.

Responsibilities

~1 min read

→AI Agent & Chatbot Development
- →Design, build, and deploy conversational agents using Azure AI Foundry, Microsoft Agent Framework, and OpenAI.
→Develop prompt strategies for context-aware, multi-turn dialogue.
→Design agentic loops with task decomposition, state passing, and workflow enforcement.
→Implement structured error responses and graceful degradation in agent tool calls.
→Data & Model Engineering
- →Build and maintain data ingestion and indexing pipelines for Azure AI Search and Cosmos DB.
→Evaluate and optimize AI system performance for accuracy, scalability, latency, and cost.
→System Integration & Architecture
- →Integrate AI with enterprise systems (ConnectWise PSA, Confluence) via RESTful APIs.
→Implement MCP servers for secure, typed tool integration between agents and enterprise APIs.
→Deploy containerized services to Azure using multi-stage builds and CI/CD pipelines.
→Security, Compliance & Governance
- →Ensure AI systems comply with data privacy regulations (GDPR) and security standards.
→Implement access controls, encryption, and audit logging for AI workflows.
→Research & Innovation
- →Stay current with LLM technologies, frameworks, and methodologies.
→Evaluate emerging tools to improve solution quality and delivery speed.
→Collaboration & Leadership
- →Work with cross-functional teams to recommend LLM-driven solutions.
→Contribute to architectural decisions aligned with strategic goals.
→Mentor junior engineers and support knowledge sharing.
→Operational Excellence
- →Own incident resolution and bug fixes.
→Create and maintain technical documentation for AI systems and integrations.

Requirements

~1 min read

Understanding of AI solution architecture patterns: LLM gateway/service layer, retrieval layer, orchestration/agent layer, and evaluation/observability layer.
Experience testing AI-enabled components using deterministic seams (mocked LLM clients, retrievers, tools), scenario-based tests, and regression/evaluation datasets for non-deterministic outputs.
Understanding of retrieval systems (keyword search, vector search, embeddings) and ranking algorithms
Familiarity with AI/ML versioning practices: prompt version control with semantic tagging, evaluation baseline tracking, dataset versioning, experiment reproducibility.
Familiarity with deploying, monitoring, and maintaining ML models in production environments.
Experience with Azure Cosmos DB, Azure Container Apps, Docker containerization
Familiarity with CI/CD pipelines and DevOps practices.
AI-102
Experience with Application Insights telemetry, distributed tracing.
Prior experience integrating AI agents with enterprise platforms like ConnectWise PSA, Confluence, SPO, etc.

Atlas Technica is proud to be an Equal Employment Opportunity employer. We do not discriminate based upon race, religion, color, national origin, gender, sexual orientation, gender identity, age, status as a protected veteran, status as an individual with a disability, or other applicable legally protected characteristics.