- 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 founders mindset and love solving ambiguous, high-impact technical challenges.

- Bachelors or Masters in Computer Science, Machine Learning, or a related technical discipline