About The Role

Job Description & Summary: A career within Technology Consulting services will provide you with the opportunity to bring our clients a competitive advantage through defining their technology objectives, assessing solution options, and devising architectural solutions that help them achieve both strategic goals and meet operational requirements. We help build software and design data platforms, manage large volumes of client data, develop compliance procedures for data management, and continually research new technologies to drive innovation and sustainable change. We are looking for a hands-on GenAI Developer to design, build, test, and deploy Generative AI applications using large language models, retrieval-augmented generation, semantic search, vector databases, prompt engineering, FastAPI-based services, and agentic frameworks. The role will contribute to enterprise chatbots, knowledge assistants, document intelligence, automation agents, code assistants, and AI-led business process automation. Job Position Title: IN_ Sr. Associate_ GenAI with Python Developer _AppTech_Advisory_ Kolkata (Immediate joiners) Responsibilities: GenAI Application Development · Develop GenAI applications using LLM APIs, prompt templates, structured outputs, RAG pipelines, embeddings, vector search, and tool calling. · Build conversational AI, enterprise search, document Q&A, summarization, classification, data extraction, and workflow automation use cases. · Integrate GenAI features with backend applications, portals, APIs, databases, document stores, and enterprise systems. · Implement prompt orchestration, context management, response formatting, source citation handling, and feedback capture. RAG, Semantic Search & Knowledge Ingestion · Build document ingestion pipelines including parsing, OCR coordination, chunking, metadata extraction, embedding generation, indexing, and refresh. · Implement semantic search and hybrid search using vector databases and search platforms. · Support retrieval optimization through metadata filtering, chunking strategy, re-ranking, grounding, and citation generation. · Work with enterprise knowledge sources such as SharePoint, Confluence, Google Drive, S3, databases, CRM, ITSM tools, and document repositories. Agentic AI Development · Develop basic to intermediate agentic workflows using LangChain, LangGraph, CrewAI, LlamaIndex, or equivalent frameworks. · Implement agents with tools, memory, reasoning steps, API actions, and human-in-the-loop checkpoints. · Build semi-autonomous workflows for business process automation while following safety and approval guardrails. Backend, API & Data Engineering · Develop backend services using Python, FastAPI, Node.js, or similar technologies. · Build REST APIs for LLM orchestration, retrieval, ingestion, evaluation, semantic search, and agent execution. · Work with SQL and NoSQL databases such as PostgreSQL, MySQL, SQL Server, MongoDB, Cosmos DB, DynamoDB, or equivalent. · Use Redis or equivalent caching for session state, conversational memory, frequently accessed retrieval results, rate limiting, and workflow state. · Follow secure coding, logging, exception handling, request validation, OpenAPI documentation, and production deployment practices. Testing, Evaluation & Observability · Support prompt testing, LLM response validation, RAG evaluation, regression testing, and hallucination checks. · Use tools such as LangSmith, Langfuse, RAGAS, DeepEval, TruLens, Promptfoo, or equivalent under guidance. · Capture logs, traces, token usage, latency, cost, feedback, and quality signals for GenAI applications. Mandatory skill sets: CORE GENAI - LLM APIs, prompt engineering, embeddings, RAG, semantic search, vector databases, structured outputs, grounding (Hands-on). Frameworks - LangChain, LangGraph, CrewAI, LlamaIndex, Langflow (Hands-on). Backend APIs - Python, FastAPI, Node.js, REST APIs, OpenAPI, async processing (Hands-on). Data Stores - SQL, NoSQL, vector DB, Redis caching, object storage (Working knowledge). Cloud AI - Azure OpenAI, AWS Bedrock, GCP Vertex AI, OpenAI, Anthropic Claude, Gemini, Hugging Face (Working knowledge). Document Processing - PDF, Word, Excel, HTML, OCR, chunking, metadata extraction, indexing (Working knowledge). Evaluation & Observability - LangSmith, Langfuse, RAGAS, Promptfoo, OpenTelemetry basics (Basic to working knowledge). Security & Responsible AI - PII handling, access control, prompt injection awareness, content filtering, audit logging (Basic knowledge). Soft Skills · Strong problem-solving and analytical thinking. · Ability to work in agile delivery teams and collaborate with cross-functional stakeholders. · Good communication and documentation skills. · Ability to learn and evaluate fast-evolving GenAI tools, frameworks, and patterns. Preferred Skills: Exposure to Kafka or messaging systems for asynchronous processing. · Exposure to MCP server development, tool integration patterns, or agent-to-tool communication. · Basic understanding of A2A communication patterns and multi-agent orchestration. · Familiarity with Docker, CI/CD, GitHub Actions, Azure DevOps, Jenkins, or equivalent. · Exposure to fine-tuning, SLMs, vLLM, Ollama, or Hugging Face model deployment. · Frontend integration awareness using React, Angular, Streamlit, Gradio, or similar. · Domain exposure in banking, healthcare, insurance, retail, telecom, or enterprise operations. Years of experience required: 5+ years (with 1+ years in GenAI/LLM ecosystems) Education qualification: B.E. / <B.Tech> / MCA/ M.E/ <M.TECH/> MBA/ PGDM. All qualifications should be in regular full-time mode with no extension of course duration due to backlogs.