Development Lead - Advanced RPA

• Lead end-to-end design and delivery of GenAI, RAG, Agentic RAG, AI assistant, chatbot, document intelligence, and automation agent solutions.

• Define agentic AI architecture patterns including tool calling, function calling, planning, memory, reflection, multi-agent orchestration, workflow routing, and human-in-the-loop controls.

• Guide teams in building robust RAG pipelines covering ingestion, parsing, chunking, metadata design, embeddings, vector indexing, hybrid retrieval, reranking, query rewriting, and grounded response generation.

• Provide hands-on technical leadership using Python, APIs, microservices, LangChain, LangGraph, LlamaIndex, Semantic Kernel, AutoGen, CrewAI, or similar frameworks.

• Lead integration of LLMs with enterprise applications, knowledge repositories, databases, workflow platforms, search services, and cloud-based AI services.

• Establish standards for prompt engineering, prompt versioning, reusable prompt libraries, structured outputs, tool integration, evaluation datasets, and model configuration management.

• Drive LLMOps and GenAIOps practices including observability, tracing, monitoring, regression testing, latency optimization, token usage tracking, cost optimization, and production support readiness.

• Ensure Responsible AI, security, privacy, governance, access control, prompt injection mitigation, PII handling, auditability, and compliance controls are embedded in solution design.

• Review solution designs, code, prompts, retrieval logic, evaluation results, deployment plans, and production issues to maintain engineering quality.

• Mentor developers, conduct technical reviews, support estimation and planning, coordinate delivery, and communicate solution progress to stakeholders.

• Strong hands-on experience in

, APIs, microservices, backend integration, modular application design, debugging, and production-grade development practices.

• Strong experience with Generative AI, Large Language Models, prompt engineering, embeddings, token management, structured outputs, and LLM-based application development .

• Hands-on experience designing and implementing

using enterprise documents, knowledge repositories, vector databases, semantic search, hybrid retrieval, reranking, and grounded generation.

• Strong understanding of

including planning, reasoning loops, tool calling, function calling, memory, autonomous workflows, multi-agent systems, supervisor-agent patterns, and human-in-the-loop design.

• Hands-on experience with agentic frameworks such as

, or equivalent orchestration frameworks.

• Experience integrating LLMs through cloud AI services and model APIs such as AWS Bedrock, Amazon SageMaker, Azure OpenAI, Google Vertex AI, OpenAI APIs, Anthropic APIs, or open-source model endpoints.

• Strong knowledge of vector databases or semantic search platforms such as FAISS, Pinecone, Chroma, Weaviate, OpenSearch, Azure AI Search, Qdrant, or pgvector.

• Working knowledge of LLMOps, GenAIOps, model evaluation, hallucination control, guardrails, observability, prompt/version management, and cost optimization practices.

• Ability to lead technical discussions, perform design reviews, mentor engineers, estimate work, manage technical risks, and communicate with stakeholders.

• Experience leading GenAI delivery teams or technical workstreams for enterprise AI use cases.

• Exposure to AgentOps, AI gateways, MCP-based tool integration, GraphRAG, knowledge graphs, ontology-driven retrieval, or advanced reasoning patterns.

• Experience with FastAPI, Flask, Streamlit, React, Node.js, Docker, Kubernetes, CI/CD, and cloud deployment of AI-enabled applications.

• Knowledge of OCR, document intelligence, NLP, text classification, summarization, entity extraction, search, automation, and enterprise workflow use cases.

• Cloud or AI certifications across AWS, Azure, Google Cloud, Generative AI, Machine Learning, Data Science, or Solution Architecture are preferred.

of relevant experience in GenAI development, AI engineering, Python/backend development, ML/NLP application development, enterprise automation, or technical leadership roles.

• Candidates should have hands-on experience building, integrating, evaluating, or deploying GenAI, RAG, Agentic RAG, AI assistant, chatbot, document intelligence, or automation agent solutions.

• Candidates should demonstrate technical leadership through solution design, code reviews, mentoring, delivery ownership, stakeholder communication, and quality governance.

• B.E. / B.Tech in Computer Science, Information Technology, Artificial Intelligence, Data Science, Electronics, Software Engineering, or any other relevant engineering stream.

• BCA / MCA / M.Tech / M.Sc. in Computer Science, Information Technology, Artificial Intelligence, Data Science, Machine Learning, Software Engineering, or related disciplines from a recognized institution or university.