Key Responsibilities
Develop GenAI applications using Python, LLM APIs, prompt engineering, RAG patterns, embeddings, vector search, and agentic AI frameworks- .Build AI agents capable of reasoning, planning, tool calling, function calling, workflow orchestration, memory usage, task decomposition, and multi-step execution
- .Design and implement RAG and Agentic RAG pipelines using document ingestion, parsing, chunking, metadata tagging, embeddings, vector indexing, semantic search, hybrid retrieval, reranking, prompt construction, and grounded response generation
- .Integrate GenAI solutions with structured and unstructured enterprise data sources such as documents, databases, SharePoint repositories, knowledge bases, APIs, ticketing systems, and workflow platforms
- .Implement prompt templates, system prompts, reusable prompt libraries, structured outputs, JSON response formats, prompt versioning, and output validation logic
- .Create tool integrations that allow agents to call APIs, execute workflows, retrieve data, summarize content, classify information, generate reports, and trigger downstream actions safely
- .Support model selection and configuration based on use case needs such as accuracy, latency, context window, token usage, cost, privacy, and deployment constraints
- .Integrate LLMs with enterprise systems, APIs, databases, knowledge repositories, search services, and automation workflows
- .Use frameworks such as LangChain, LangGraph, LlamaIndex, Semantic Kernel, AutoGen, CrewAI, or similar tools to build agentic workflows
- .Create reusable components for prompt templates, tool integrations, retrieval workflows, memory handling, guardrails, model evaluation, tracing, observability, and monitoring
.
Mandatory Technical Skil
- ls
Strong programming capability in Python, including data structures, APIs, object-oriented programming, exception handling, logging, debugging, package management, and modular application developme - nt.Hands-on exposure to Generative AI, Large Language Models, prompt engineering, embeddings, tokenization, context windows, structured outputs, and AI application developme
- nt.Working knowledge of RAG architecture, including document processing, chunking strategies, metadata design, vectorization, semantic search, hybrid search, reranking, context augmentation, and grounded response generati
- on.Experience or strong project exposure in agentic AI concepts such as tool calling, function calling, planning, memory, reflection, reasoning loops, task decomposition, autonomous execution, human-in-the-loop flows, and workflow orchestrati
- on.Exposure to frameworks such as LangChain, LangGraph, LlamaIndex, Semantic Kernel, AutoGen, CrewAI, or equivalent agentic development framewor
- ks.Experience integrating LLMs through APIs or cloud AI services such as AWS Bedrock, Azure OpenAI, Google Vertex AI, OpenAI APIs, Anthropic APIs, or open-source model endpoin
ts.
Preferred / Additional Sk
- ills
Exposure to cloud-native AI services, especially AWS Bedrock, Amazon SageMaker, Azure OpenAI, Azure AI Search, Google Vertex AI, or Gemini - APIs.Familiarity with multi-agent systems, supervisor-agent patterns, planner-executor workflows, human-in-the-loop flows, and agent evaluation met
- hods.Knowledge of LLMOps or GenAIOps practices, including prompt versioning, model configuration management, monitoring, tracing, evaluation, and cost trac
king.
Experience Cr
- iteria
1 to 4 years of relevant experience in GenAI development, AI application engineering, Python development, ML/NLP application development, backend development, or automation engin - eering.Candidates with 0–1 year of experience should demonstrate capability through academic projects, internships, certifications, GitHub repositories, hackathons, prototypes, or hands-on GenAI exper
- iments.Candidates with 2–5 years of experience should have hands-on experience building, integrating, testing, or deploying GenAI, AI assistant, chatbot, RAG, automation, or agentic workflow sol
utions.
Mandatory Qualif
- ication: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 uni
versity.
