Lead AI Engineer

We are seeking a Lead AI Engineer with strong software engineering foundations and hands-on experience in Generative and Agentic AI. In this role, you will architect, design and build production-grade AI systems powered by large language models and intelligent agents.

You will design scalable AI workflows, integrate autonomous agents into real-world applications, and help translate cutting-edge AI capabilities into reliable business solutions.

8+ years of professional software development experience (Dotnet/React/Full-stack) with mentorship/team leadership experience.
- Minimum 2 years of hands-on experience building applications using Generative AI or agentic AI systems.
- Strong proficiency in Python and backend engineering principles.
- Experience designing RESTful APIs and scalable service architectures.
- Practical experience with LLM APIs, embeddings, vector search, or RAG pipelines.
- Working knowledge of AI governance principles, responsible AI practices, or compliance considerations.
- Familiarity with agent frameworks and autonomous decision workflows.
- Understanding of data structures, system design, and performance optimization.
- Strong analytical thinking and problem-solving skills.

Preferred Qualifications
- Experience deploying AI applications in cloud environments (preferably AWS).
- Familiarity with vector databases and knowledge retrieval architectures.
- Experience working with Git, CI/CD pipelines, and production deployments.
- Exposure to containerization and orchestration tools (Docker, Kubernetes).
- Experience implementing evaluation or monitoring pipelines for AI agents.
- Knowledge of AI safety, guardrails, and responsible AI practices.
- Knowledge of distributed systems or event-driven architecture.

Architect and Design production-ready applications leveraging Generative AI and agentic AI frameworks.
- Build, Deploy and Monitor intelligent workflows using LLMs, multi-agent coordination, and orchestration pipelines.
- Integrate new AI applications with traditional pre-existing applications.
- Implement prompt engineering strategies, retrieval-augmented generation (RAG), and contextual memory systems.
- Implement various AI coding techniques (context-driven, spec-driven development etc.).
- Architect and implement AI agents capable of reasoning, planning, and multi-step task execution.
- Implement and utilize Model Context Protocol (MCP) patterns to enable structured communication between agents, tools, and systems.
- Provide mentoring and technical guidance to developers and other AI Engineers.
- Develop evaluation pipelines to measure accuracy, safety, and performance of AI systems.
- Optimize latency, cost efficiency, and scalability of AI-powered workflows.
- Collaborate with product managers, data teams, and software engineers to deliver AI features.
- Ensure reliability through testing, logging, monitoring, and observability of AI behavior.
- Own architectural decisions, coding standards, and best practices.
- Act as an AI trailblazer for development teams by promoting best practices, reusable patterns, and adopting AI-driven engineering approaches.
- Apply AI governance principles to ensure responsible model usage, auditability, and transparency in AI workflows.
- Support implementation of governance controls related to data handling, model behavior monitoring, and risk mitigation.
- Research emerging AI tools and frameworks to continuously improve system capabilities.
- Document AI workflows and maintain reproducible engineering processes.