- 8+ years of professional software engineering experience in Python.

- 2+ years of hands on exposure of building production grade GenAI solutions.

- Hands-on experience with Agents, RAG, TensorFlow, PyTorch, Hugging Face, and LangChain

- Strong hands on exposure of Python and FastAPI development

- Deployed containerized apps to Kubernetes (GKE preferred); comfortable with Docker, Helm, basic K8s concepts.

- Built and fine tune and optimize LLMs.

- Expert at writing clear, reproducible prompts and familiar with advanced patterns (ReAct, Plan-and-Execute, Tree-of-Thought, etc.).

- Have good understanding of GenAI solutions deployment and monitoruing in production evnironment.

- Mentor team members on AI/ML, MLOps, and cloud best practices.

- Comfortable with modern engineering practices: Git, CI/CD, testing (pytest + LLM eval), code reviews and Agile delivery methodology

Roles & Responsibilities :

- Design, build, and deploy AI/ML and Generative AI solutions using TensorFlow, PyTorch, Hugging Face, and LangChain.

- Develop agentic and multimodal AI systems for customer engagement, document processing, and analytics.

- Fine-tune and optimize LLMs (e.g., OpenAI, Llama, Mistral, Gemini) for domain-specific use cases (preferred).

- Deploy scalable AI/ML services on AWS, Azure, and Kubernetes using Docker and Terraform.

- Monitor, benchmark, and optimize AI workloads for performance, reliability, and cost.

- Build data pipelines for preprocessing, augmentation, and feature extraction across image, audio, and text data.

- Develop RESTful APIs and microservices to expose AI/ML capabilities.

- Collaborate with cross-functional teams (QA, product, data engineering) to deliver impactful AI/ML solutions.

- Lead knowledge-sharing sessions and contribute to open-source or internal AI communities.

- Strong collaboration, communication, mentoring, and knowledge-sharing abilities.