Python Developer

Role: GenAI & Agentic AI

Location: Bangalore

Experience: 10 Years+

About the Role

Generative AI (GenAI), Agentic AI, and modern LLM (Large Language Model) ecosystems. The ideal candidate will have hands-on experience with LangChain, LangGraph, MCP, AgentOps, RAG pipelines, fine-tuning models, and MLOps practices, along with proficiency in cloud deployment (AWS, Azure AI, Bedrock, etc.). You will be responsible for building, optimizing, and deploying AI-driven solutions that solve real-world business problems at scale.

Key Responsibilities

• Design & Develop GenAI Applications: Build scalable AI applications using Python, integrating LangChain, LangGraph, MCP, and AgentOps frameworks.
• LLM Integration: Work with multiple LLM providers (Azure AI, AWS Bedrock, OpenAI, Anthropic, etc.) for text, multimodal, and agent-based workflows.
• RAG Implementation: Architect and deploy Retrieval-Augmented Generation pipelines, integrating vector databases and knowledge graphs.
• Fine-tuning & Model Ops: Fine-tune LLMs for domain-specific tasks, implement MLOps pipelines for continuous integration, testing, and monitoring.
• Agentic AI Development: Design multi-agent systems with task orchestration, memory handling, and error recovery.
• Deployment & Cloud Infrastructure: Deploy applications on AWS cloud (EC2, Lambda, S3, Bedrock, SageMaker, etc.) and Azure AI services.
• Performance Optimization: Ensure model efficiency, latency reduction, and cost optimization in production environments.
• Collaboration: Work closely with cross-functional teams (Data Scientists, DevOps, Product Owners) to deliver high-quality AI solutions.

Required Skills & Qualifications

• Strong proficiency in Python with experience in backend development.
• Hands-on experience with GenAI frameworks: LangChain, LangGraph, MCP, AgentOps.
• Knowledge of RAG (Retrieval-Augmented Generation) pipelines and vector databases (Pinecone, Chroma, Weaviate, FAISS).
• Experience in fine-tuning and prompt engineering for LLMs.
• Strong understanding of MLOps (CI/CD for ML, model deployment, monitoring).
• Experience with cloud AI platforms: Azure AI, AWS Bedrock, AWS SageMaker, GCP Vertex AI (preferred).
• Knowledge of Agentic AI concepts – multi-agent orchestration, planning, memory.
• Familiarity with Docker, Kubernetes, Terraform, and GitOps practices.
• Strong problem-solving and debugging skills.

Preferred Qualifications

• Prior experience with multi-modal models (text, images, audio).
• Exposure to enterprise AI compliance and security best practices.
• Familiarity with ISO/IEC AI governance frameworks is a plus.
• Open-source contributions in AI/ML projects.