Senior AI Engineer – Python, RAG, Agentic AI, ADK, MCP, GCP, Vertex AI, IBM Watsox
- Compensation
- Not disclosed
- Location
- IN - TDC 1 (IN110)
- Work setup
- Not specified
- Seniority
- Senior
- Employment
- Full time
- Experience
- 1+ years
- Category
- Ai engineering
- Posted
Skills
Must have
- Relevant experience
Nice to have
- Experience implementing Model Context Protocol (MCP) for agent coordination.
- Prior exposure to Lang Graph, AutoGen, or related orchestration frameworks.
- Knowledge of MLOps best practices (CI/CD for ML, observability, monitoring, scaling).
- Familiarity with responsible AI principles (safety, fairness, interpretability).
- Experience in enterprise-scale deployments of AI-driven platforms.
- Contributions to open-source AI/ML projects are a plus.
Full description
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Job Description:Job Summary
We are seeking a highly skilled AI Engineer experience in Software Development, Data Science, or Machine Learning to design, develop, and deploy cutting-edge AI systems leveraging Large Language Models (LLMs), Chatbots, Retrieval-Augmented Generation (RAG), and agentic AI architectures.
This role involves hands-on development with LLMs, embeddings, RAG pipelines, and multi-agent systems using modern frameworks like LangChain, LangGraph, and LlamaIndex. The ideal candidate has experience with Vertex AI on GCP and IBM WatsonX, fine-tuning, and Agent Development Kits (ADKs), and is excited about building scalable, production-grade AI platforms.
Responsibilities
- Agentic AI Development:
Design, build, and deploy agentic AI systems using frameworks such as LangChain, LangGraph, and related libraries.
Develop and deploy multi-agent systems capable of autonomous decision-making, reasoning, planning, and collaboration.
- RAG Pipelines:
Implement and optimize retrieval-augmented generation (RAG) systems, ensuring agents can access and incorporate external knowledge sources for grounded, accurate responses.
- LLM Engineering:
Fine-tune and prompt-engineer LLMs for task-specific reasoning, planning, and dynamic adaptation.
Work with LLM/SLM APIs, embeddings, and advanced generative AI techniques.
- Enterprise AI Platform:
Lead the development of enterprise-grade AI platforms integrating LLMs, RAG, embeddings, and agentic AI protocols.
Implement and standardize Model Context Protocol (MCP) for consistent context management across models and agents.
- MLOps & Observability:
Establish and enforce best practices for MLOps, monitoring, and observability, ensuring scalable and maintainable AI solutions.