Lead AI Engineer

What You’ll Do Architect, build, and optimize production-grade Generative AI applications using modern frameworks such as LangChain, LlamaIndex, Semantic Kernel, or custom orchestration layers.

Lead the design of Agentic AI frameworks (Agno, AutoGen, CrewAI etc.), enabling intelligent, goal-driven workflows with memory, reasoning, and contextual awareness.

Develop and deploy Retrieval-Augmented Generation (RAG) systems integrating LLMs, vector databases, and real-time data pipelines.

Design robust prompt engineering and refinement frameworks to improve reasoning quality, adaptability, and user relevance.

Deliver high-performance backend systems using Python (FastAPI, Flask, or similar) aligned with SOLID principles, OOP, and clean architecture.

Own the complete SDLC, including design, implementation, code reviews, testing, CI / CD, observability, and post-deployment monitoring.

Use AI-assisted environments (e.g., Cursor, GitHub Copilot, Claude Code) to accelerate development while maintaining code quality and maintainability.

Collaborate closely with MLOps engineers to containerize, scale, and deploy models using Docker, Kubernetes, and modern CI / CD pipelines.

Integrate APIs from OpenAI, Anthropic, Cohere, Mistral, or open-source LLMs (Llama 3, Mixtral, etc.).

Leverage VectorDB such as FAISS, Pinecone, Weaviate, or Chroma for semantic search, RAG, and context retrieval.

Develop custom tools, libraries, and frameworks that improve development velocity and reliability across AI teams.

Partner with Product, Design, and ML teams to translate conceptual AI features into scalable user-facing products.

Provide technical mentorship and guide team members in system design, architecture reviews, and AI best practices.

Lead POCs, internal research experiments, and innovation sprints to explore and validate emerging AI techniques.

What You Bring

7–12 years of total experience in software development, with at least 3 years in AI / ML systems engineering or Generative AI.

Python experience with strong grasp of OOP, SOLID, and scalable microservice architecture.

Proven track record developing and deploying GenAI / LLM-based systems in production.

Hands-on work with LangChain, LlamaIndex, or custom orchestration frameworks.

Deep familiarity with OpenAI, Anthropic, Hugging Face, or open-source LLM APIs.

Advanced understanding of prompt construction, optimization, and evaluation techniques.

End-to-end implementation experience using vector databases and retrieval pipelines.

Understanding of MLOps, model serving, scaling, and monitoring workflows (e.g., BentoML, MLflow, Vertex AI, AWS Sagemaker).

Experience with GitHub Actions, Docker, Kubernetes, and cloud-native deployments.

Are obsessed with clean code, system scalability, and performance optimization.

Can balance rapid prototyping with long-term maintainability.

Excel at working independently while collaborating effectively across teams.

Stay ahead of the curve on new AI models, frameworks, and best practices.

Have a founder’s mindset and love solving ambiguous, high-impact technical

challenges.

Bachelor’s or Master’s in Computer Science, Machine Learning, or a related

technical discipline