AIML Developer

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 Pyt hon, 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 developm e nt.Working knowledge of RAG architect ure, 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 conce pts 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, Cre wAI, 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.Candidate s with 0–1 year of exp erience should demonstrate capability through academic projects, internships, certifications, GitHub repositories, hackathons, prototypes, or hands-on GenAI exper iments.Candidate s with 2–5 years of exp erience 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.