Note: Please apply only if you can provide the applicable details such as Candidate PF Number, ESI/Medical Insurance, and EPFO Registration.
Role: GenAI & Agentic AI
Experience: Mind level 5–7 years
Role- Sr GenAI & Agentic AI
Experience: 9+ yrs
Location- Onsite Bangalore
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.
