AI Engineer

Atlas Technica is a leading managed services and technology firm serving hedge funds, private equity firms, family offices, and other alternative investment businesses. We design, secure, and operate technology platforms for clients where confidentiality, availability, and trust are non-negotiable.

Our AI & Advisory practice helps clients move from AI questions to working evidence. We pair senior architecture with hands-on engineering so clients can test useful ideas quickly, reject weak ideas early, and move successful prototypes toward secure production use.

We are seeking an AI Engineer in Ukraine to work side by side with Atlas's Principal and Senior AI Architects. The architect defines the technical direction, security boundaries, and production decisions. You turn those decisions into working software, integrations, deployment patterns, tests, and evidence.

This is not a research-only or prompt-only role. You will write production-quality code, build rapid proofs of concept, integrate enterprise systems, deploy services in Azure, test controls and failure modes, and document what remains before broader use.

The work will vary across clients. One engagement may involve an MCP server and database integration. Another may require reviewing an internal Python application, validating Microsoft Graph permissions, building a controlled retrieval workflow, or moving a prototype into Azure Container Apps. You must be comfortable learning quickly without treating every client as a custom snowflake.

• 5+ years of professional software engineering experience, including recent hands-on work with AI-enabled applications, data systems, cloud applications, or enterprise integrations.
• Strong Python development skills and experience building APIs, services, scripts, or data workflows.
• Experience with at least one additional production stack such as C# and .NET, TypeScript and Node.js, or Java.
• Hands-on Azure experience, including application hosting, identity, storage, databases, secrets, logging, and deployment.
• Practical experience with LLM APIs, agentic workflows, retrieval-augmented generation, embeddings, prompt design, structured outputs, and model limitations.
• Experience building or integrating APIs, connectors, MCP components, databases, or enterprise workflow systems.
• Strong understanding of authentication, authorization, role-based access control, least privilege, environment separation, and secure secret handling.
• Experience with Git, CI/CD, Docker, automated testing, and production troubleshooting.
• Ability to write clean, maintainable, testable code and explain implementation decisions.
• English level B2 or higher, with the ability to participate in technical client calls and write clear documentation.
• Strong ownership and follow-through in a remote delivery environment.

Build Rapid Proofs and Production Components

• Build bounded proofs of concept for research, data access, document analysis, workflow automation, reconciliation, and internal application use cases.
• Convert approved prototypes into maintainable application components and deployment patterns.
• Review, refactor, test, and extend existing Python, C#, TypeScript, or vendor-developed code.
• Implement structured outputs, validation, retries, fallbacks, and human-review steps for AI-enabled workflows.
• Optimize solutions for reliability, latency, cost, maintainability, and operational support.

Integrate AI With Enterprise Systems

• Build and test REST APIs, MCP servers and clients, connectors, webhooks, and data-integration services.
• Connect approved AI workflows to Microsoft 365, SharePoint, Microsoft Graph, databases, data warehouses, file stores, and third-party platforms.
• Build data ingestion, extraction, normalization, retrieval, indexing, and synchronization components.
• Implement retrieval using Azure AI Search, vector or hybrid search, embeddings, metadata filtering, and access-aware patterns.
• Work with Azure SQL, PostgreSQL, Cosmos DB, Snowflake, or comparable enterprise data stores as required.

Deploy and Operate in Azure

• Deploy services using Azure Functions, Azure Container Apps, App Service, virtual machines, Azure AI Foundry, and related services based on the approved architecture.
• Create repeatable builds and releases using Git, GitHub Actions or Azure DevOps, CI/CD pipelines, Docker, and infrastructure automation.
• Configure application identity, role-based access, secrets, certificates, network paths, storage, and environment separation.
• Implement logging, monitoring, telemetry, alerting, and distributed tracing using Azure-native tools such as Application Insights.
• Support incident diagnosis, bug fixes, controlled releases, recovery testing, and technical handoff.

Test AI Quality and Security

• Validate identity, permissions, data boundaries, logging, code execution, and human-review controls.
• Test for prompt injection, data exfiltration, unsafe tool use, excessive permissions, malformed outputs, model failure, and dependency failure.
• Build scenario-based tests, deterministic seams, mocked tools or model clients, regression datasets, and evaluation baselines.
• Document what passed, what failed, what remains uncertain, and what must change before production use.
• Treat vendor claims as hypotheses to test, not facts to repeat.

Work as Part of the Architecture Pair

• Work under the technical direction of a Principal or Senior AI Architect.
• Ask clarifying questions when requirements, access, success criteria, or ownership are unclear.
• Translate architecture decisions into working technical evidence quickly.
• Surface blockers, scope changes, and capacity risks before they affect delivery.
• Participate in design reviews, code reviews, client technical sessions, and production-readiness assessments.
• Create clear implementation notes, diagrams, runbooks, and handoff documentation.

Help Build the Offshore AI Center of Excellence

• Contribute reusable code, deployment templates, test harnesses, connector patterns, and technical documentation.
• Improve engineering standards and help reduce repeat work across client engagements.
• Share lessons from failures and production issues with the broader Ukraine-based AI team.
• Mentor more junior engineers as the team grows.

• Azure AI Foundry, Azure OpenAI, Azure AI Search, Microsoft Agent Framework, or comparable orchestration experience.
• Microsoft Graph, SharePoint Online, Entra ID, Microsoft 365, Copilot Studio, or Power Platform experience.
• Experience with Azure Functions, Azure Container Apps, App Service, Cosmos DB, PostgreSQL, Azure SQL, Snowflake, or Cloudflare.
• Experience testing non-deterministic AI components with evaluation datasets and regression methods.
• Experience moving citizen-developed, vendor-developed, or prototype applications toward production readiness.
• Experience serving financial services, regulated industries, or enterprise clients.
• AI-102 or relevant Azure certification.