Senior AI Engineer

We are seeking a Senior AI Engineer to lead the design and development of AI-powered accelerators that streamline and automate complex business workflows, with a strong focus on data-migration programs.

This role centers on building reliable, production-grade agentic systems and orchestration frameworks that improve speed, quality and reusability across migration and modernization initiatives. The ideal candidate combines strong hands-on engineering capability with architectural leadership and a passion for enabling teams through shared accelerators, assets, and patterns.

Roles & Responsibilities:

• Design, build and maintain AI accelerators that enhance productivity and reduce delivery effort across data-migration workflows.
• Develop agentic and workflow-automation solutions (multi-agent orchestration, task routing, tool-use pipelines, etc.).
• Implement robust context-management strategies (retrieval pipelines, memory stores, episodic context, session-state control).
• Architect reliable production systems including evaluation, telemetry, failure-handling, and safety/guardrail patterns.
• Partner with engineering and delivery teams to adapt accelerators to live projects and real-world migration scenarios.
• Provide architectural guidance, mentorship, and code reviews for junior and mid-level engineers working on related accelerators.
• Establish development standards, reusable frameworks, and reference implementations to support scale and reuse.
• Collaborate with product and delivery stakeholders to translate business workflows into automatable system designs.
• Contribute to documentation, training materials, and onboarding guidance for accelerator adoption across teams.

Requirements

Qualifications :

4+ years of professional software engineering experience, with at least 2–3 years in applied AI / LLM systems.

Experience working in consulting, platform engineering, or accelerator-style reusable asset development.

Prior involvement in data-migration, modernization, or analytics engineering programs is a strong plus.

Strong expertise in LLM-based application development (frameworks such as LangChain, LangGraph, Semantic Kernel, custom orchestration).

Proven experience with agentic design patterns, including:

• Multi-step task planning & decomposition
• Tool-calling / API-driven agents
• Workflow graphs & supervisory agents
• Long-running task coordination

Deep familiarity with context & retrieval design, including:

• RAG pipelines and embedding strategies
• Retrieval quality evaluation
• Grounding and prompt-context isolation
• Vector stores & document indexing

Strong software engineering fundamentals:

• Python (primary) — testing, packaging, dependency management
• Architectural design & code quality best-practices
• CI/CD and deployment patterns
• Experience designing observability and evaluation loops for AI workflows (telemetry, metrics, regression testing, drift tracking).

Experience integrating LLM systems with:

• Data platforms / warehouses
• Metadata systems and workflow tools
• REST / Microservices architectures