Roles & Responsibilities:

• Design, develop, and deploy LLM-powered applications and pipelines
• Build and optimize Agentic AI workflows (multi-agent systems, orchestration, tool integrations)
• Implement MCP (Model Context Protocol) for secure, standardized LLM–tool integrations
• Work with large datasets to enable scalable, high-performance GenAI applications
• Implement prompt engineering, fine-tuning, and RAG pipelines
• Collaborate with cross-functional teams to deliver production-ready GenAI solutions

 Skills & Knowledge:

·      Strong expertise in Large Language Models (LLMs) and Generative AI

·      Proficiency in Python and GenAI frameworks (LangChain, LangGraph, OpenAI APIs)

·      Hands-on experience with Agentic AI architectures (multi-agent orchestration, tool/function calling, reasoning engines)

·      Proven expertise in MCP (Model Context Protocol) for building extensible AI agents

·      Experience with cloud platforms (AWS Bedrock/SageMaker, Azure OpenAI, or GCP Vertex AI)

·      Familiarity with Docker, Kubernetes, GitHub Actions/Jenkins for scalable deployments

·      Solid understanding of vector databases, embeddings, and RAG pipelines

Requirements:

·      3–5 years of experience in designing and implementing machine learning solutions in a production environment.

·      Proven track record of delivering AI/ML projects from concept to production.

·      Ability to work collaboratively in cross-functional teams.

Education & Certification:

·      Bachelor’s/Master’s in Computer Science, AI/ML, Data Science, or related field

·      Certifications in AI/ML, Cloud, or GenAI (preferred but not mandatory)

Preferred Certifications:

·      Machine Learning or AI certifications (e.g., AWS Certified Machine Learning – Specialty, Google Cloud Professional ML Engineer, Microsoft Certified Azure AI Engineer Associate).

·      Big Data or Cloud certifications are an added advantage.