GenAI with Python Developer

Job Position Title: GenAI with Python Developer_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.