Python AI Engineer (GenAI / Agentic AI)

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Role & responsibilities

• Design, develop, and deploy production-grade GenAI and Agentic AI solutions.
• Build and manage multi-agent systems including planner, retriever, reasoning, and guardrail agents.
• Design end-to-end RAG pipelines from data ingestion to retrieval and generation.
• Develop agent orchestration workflows using LangGraph, CrewAI, LangChain, or similar frameworks.
• Optimize LLM applications for latency, accuracy, scalability, and cost efficiency.
• Implement Responsible AI practices including hallucination mitigation, prompt injection defense, and output validation.
• Collaborate with business and technical stakeholders to deliver AI-driven solutions.
• Integrate AI models with enterprise applications and APIs.
• Troubleshoot, monitor, and continuously improve AI systems in production environments.
• Participate in architecture discussions and contribute to AI best practices.

Preferred candidate profile

• 4 to 6 years of overall experience with strong Python development expertise.
• Minimum 3+ years of hands-on experience in GenAI, Agentic AI, or LLM-based applications.
• Proven experience building real-world AI systems (not POCs or demos).
• Strong understanding of RAG architecture, vector databases, embeddings, and retrieval strategies.
• Experience with LangGraph, CrewAI, LangChain, AutoGen, or similar agent frameworks.
• Knowledge of SQL and Data Analysis is highly preferred.
• Strong system design and problem-solving skills.
• Ability to explain AI architecture, design decisions, challenges, and optimization strategies.
• Experience with cloud platforms such as Azure, AWS, or GCP is an advantage.
• Excellent communication and stakeholder management skills.