Job Description – Senior/Lead AI Engineer
Position: Senior/Lead AI Engineer (Generative AI, RAG & Agentic AI)
Experience: 5+ Years
About the Role
We are seeking a talented and hands-on AI Engineer to join our growing AI team. The ideal candidate will have practical experience designing, developing, and deploying Generative AI solutions, Retrieval-Augmented Generation (RAG) applications, and Agentic AI systems. You will work on building intelligent assistants, autonomous workflows, enterprise knowledge platforms, and AI-powered business solutions, leveraging Large Language Models (LLMs).
This role requires strong Python development skills, expertise in AI frameworks such as LangChain and LangGraph, and the ability to build scalable production-grade AI applications.
Key Responsibilities
Generative AI Development
- Design, develop, and deploy AI-powered applications using Large Language Models (LLMs).
- Build conversational AI assistants, copilots, and enterprise AI solutions.
- Develop and optimize prompts, tools, and workflows to improve AI performance and accuracy.
- Evaluate and monitor AI model responses and implement guardrails for responsible AI usage.
RAG Solution Development
- Design and implement Retrieval-Augmented Generation (RAG) architectures.
- Build document ingestion, indexing, embedding, and retrieval pipelines.
- Integrate enterprise knowledge sources including databases, SharePoint, APIs, documents, and cloud storage.
- Optimize chunking, retrieval, reranking, and grounding strategies to improve answer quality.
Agentic AI Development
- Design and implement Agentic AI solutions using frameworks such as LangChain, LangGraph, CrewAI, or AutoGen.
- Build multi-agent workflows capable of planning, reasoning, tool usage, and task execution.
- Integrate AI agents with enterprise applications, APIs, and business processes.
- Develop memory management and workflow orchestration capabilities.
Software Engineering & Integration
- Develop scalable backend services using Python and FastAPI.
- Build and consume REST APIs for AI integrations.
- Implement robust error handling, logging, monitoring, and performance optimization.
- Collaborate with architects, product owners, and business stakeholders to translate requirements into AI solutions.
Cloud & Deployment
- Deploy AI applications on Azure, AWS, or GCP.
- Implement CI/CD pipelines and DevOps best practices.
- Monitor application performance, scalability, security, and operational costs.
- Follow enterprise security and governance standards.
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