The ideal candidate will be responsible for designing, developing, and deploying AI-driven solutions including GenAI POCs, intelligent agents, and scalable LLM-powered applications.
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Key Responsibilities< data-section-id="w3x26" data-start="711" data-end="741">
🔹 GenAI & LLM Development- <>Design and develop applications using Large Language Models (LLMs) for real-world use cases.
- <>Build and optimize GenAI Proof of Concepts (POCs) to demonstrate business value.
- <>Fine-tune and integrate LLMs for domain-specific applications.
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🔹 RAG & Knowledge Systems- <>Implement Retrieval-Augmented Generation (RAG) pipelines using vector databases and embeddings.
- <>Design data ingestion, chunking, and retrieval strategies for high-quality responses.
- <>Integrate structured and unstructured data sources into LLM workflows.
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🔹 Agentic AI Systems- <>Develop Agentic AI solutions capable of autonomous reasoning, planning, and execution.
- <>Build multi-agent systems and tool-using agents for complex workflows.
- <>Implement orchestration frameworks for agent collaboration.
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🔹 MCP & AI Architecture- <>Work with Model Context Protocol (MCP) concepts to manage context, memory, and tool integration.
- <>Design scalable AI architectures supporting context-aware reasoning and multi-step interactions.
- <>Ensure efficient prompt engineering and context handling strategies.
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🔹 Python Development- <>Develop backend services and AI pipelines using Python.
- <>Build APIs and microservices to expose AI capabilities.
- <>Ensure code quality, scalability, and performance optimization.
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🔹 Collaboration & Delivery- <>Collaborate with cross-functional teams (product, data, engineering) to translate business needs into AI solutions.
- <>Present POCs, demos, and technical solutions to stakeholders.
- <>Stay updated with latest advancements in GenAI and LLM ecosystems.
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Required Qualifications- <>3–8+ years of experience in Python development
- <>Hands-on experience with LLMs (OpenAI, Llama, Claude, etc.)
- <>Strong experience in RAG architecture and vector databases (Pinecone, FAISS, Weaviate, etc.)
- <>Experience building GenAI POCs or AI-driven applications
- <>Understanding of Agentic AI frameworks (LangChain, CrewAI, AutoGen, etc.)
- <>Knowledge of MCP concepts or similar context/memory frameworks
- <>Experience with REST APIs and microservices architecture
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Preferred Skills- <>Experience with prompt engineering and fine-tuning
- <>Knowledge of cloud platforms (AWS, Azure, Google Cloud Platform)
- <>Familiarity with Docker/Kubernetes for deployment
- <>Experience with streaming data, embeddings, and semantic search
- <>Understanding of AI evaluation metrics and guardrails
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Key Skills- <>Python
- <>Generative AI (GenAI)
- <>Large Language Models (LLM)
- <>Retrieval-Augmented Generation (RAG)
- <>Agentic AI
- <>MCP (Model Context Protocol)
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Soft Skills- <>Strong analytical and problem-solving skills
- <>Excellent communication and stakeholder interaction
- <>Ability to work in fast-paced, research-driven environments
- <>Self-driven and innovative mindset
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