AI Engineer

• AI Feature Development: Design, build, and deploy production-grade AI capabilities into our SaaS product, utilizing RAG pipelines, autonomous agentic tools, and Model Context Protocol (MCP).
• Architectural Engineering: Architect and maintain highly scalable, reliable backend solutions using ASP.NET Core and F#, strictly adhering to functional programming principles and event-driven architectures.
• Vector & Data Management: Implement and optimize vector databases to ensure ultra-low latency, highly accurate semantic search and contextual data retrieval.
• Model Selection & Evaluation: Evaluate, benchmark, and integrate diverse LLMs and foundation models, matching their unique capabilities to specific product requirements for optimal cost, speed, and accuracy.
• Cloud & AI Infrastructure: Orchestrate and manage secure, scalable workloads within the Azure ecosystem, specifically leveraging Azure AI deployments.

Native AI Engineer

• Consult clients during presales to assess AI readiness, constraints, and success criteria, translating visions into actionable requirements
• Prepare and present presales deliverables including architecture diagrams, assumptions, risks, estimates, scalability considerations, and implementation roadmaps
• Define AI use cases and agentic scenarios based on client needs
• Select and justify LLMs per use case based on requirements, cost, latency, safety, capability
• Architect multi-agent frameworks with orchestrator agents and MCP-style protocols
• Design and implement single-agent/multi-agent AI systems with defined roles, tool access, memory, safety boundaries
• Build orchestration logic (routing, delegation, retries, fallback strategies, consensus, human-in-the-loop flows)
• Develop RAG pipelines (data ingestion, chunking, embeddings, vector databases, hybrid retrieval, relevance optimization)
• Implement learning & feedback loops for continuous agent improvement
• Design custom or adapted models (prompt-tuned agents, LoRA fine-tuning, domain-inherited models)

Principal AI Engineer

• Build AI-assisted tooling for engineering workflows
• Improve developer productivity and operational efficiency
• Design systems that reduce repetitive manual engineering work
• Develop AI-driven solutions for pipeline analysis, failure classification, flaky test detection, release validation, and more
• Improve reliability and visibility of engineering pipelines
• Build AI systems for troubleshooting assistance and operational workflows
• Design and implement engineering copilots and internal engineering assistants
• Deploy and maintain production-grade AI services

Junior AI Engineer

• Design, train, and optimize ASR and TTS models for production use
• Design and develop AI-powered assistants
• Fine-tune and optimize large-scale models for specific business needs
• Collaborate with DevOps team to deploy the solution
• Plan and manage ML projects, articulate methodologies, and communicate results and business impact to stakeholders and customers

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Native AI Engineer

Principal AI Engineer

Junior AI Engineer

AI Engineer, Speech