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We are hiring for below exciting requirements. Please DM if any referral profiles. Software Engineer II – AI Engineer (Full Stack, Backend-Focused) LogicHive Solutions is looking for a highly skilled Software Engineer II – AI Engineer with strong expertise in Python backend development, Generative AI, LLM applications, and AI agent engineering. We are seeking an experienced engineer with 5+ years of software engineering experience and hands-on expertise in building and deploying production-grade AI-powered applications. The ideal candidate should have strong experience with LLM integration, Model Context Protocol (MCP), Retrieval-Augmented Generation (RAG), agent orchestration, and AI evaluation frameworks. The role involves building intelligent AI solutions, backend services, data pipelines, and React-based interfaces, with a strong focus on developing reliable, secure, explainable, and auditable AI applications for enterprise workflows. The engineer will work on end-to-end AI features, from prompt and agent design to API development, database integration, evaluation, and user interface implementation. Key Responsibilities 1. AI & LLM Application Development Build and maintain LLM-powered applications for document analysis, information extraction, evidence assessment, question answering, and workflow automation. Integrate Large Language Models from providers such as OpenAI, Anthropic, and Google Gemini. Implement prompt engineering, structured outputs, JSON schema validation, and function calling. Develop AI-powered features that generate reliable, evidence-based, and explainable outputs. Build solutions that support human review and validation of AI-generated results. 2. Agent Development & Orchestration Design and develop AI agents capable of using tools, retrieving relevant information, and executing multi-step workflows. Implement agent orchestration using frameworks such as LangChain, LangGraph, LlamaIndex, or similar. Develop tool execution, state management, validation, and human-in-the-loop workflows. Build conversational agents that support enterprise workflows and business processes. 3. MCP & Enterprise Tool Integration Build, integrate, and maintain Model Context Protocol (MCP) servers, clients, and tools. Connect AI applications and agents with internal enterprise systems and external services. Implement authentication, authorization, access controls, input validation, and error handling. Develop secure and reliable integrations for enterprise AI applications. 4. RAG & Retrieval Engineering Design and develop Retrieval-Augmented Generation (RAG) workflows. Implement document processing, chunking, embeddings, and vector or hybrid search. Work with vector databases and semantic retrieval techniques. Build source-grounded AI responses with proper document attribution and traceability. Optimize retrieval relevance and response accuracy. 5. Python Backend Development & APIs Develop scalable backend services using Python. Build REST APIs and backend integrations using FastAPI or Flask. Work with Pydantic, asynchronous programming, and structured data processing. Develop backend services supporting model interactions, retrieval, and agent execution. Implement robust API integrations and structured AI outputs. 6. AI Evaluation, Reliability & Observability Create evaluation datasets using human-labeled ground truth. Measure extraction accuracy, retrieval relevance, groundedness, and task completion. Implement LLM-as-judge evaluation techniques using multiple models. Develop regression testing processes for prompt, model, and retrieval changes. Implement logging, monitoring, retries, fallback mechanisms, and agent tracing. Ensure reliability and consistency of AI applications in production environments. 7. Security, Auditability & Collaboration Implement secure data-handling practices for enterprise AI applications. Maintain traceability of source evidence, model outputs, tool actions, and human review decisions. Collaborate with business stakeholders and cross-functional engineering teams to identify AI use cases. Translate business requirements into practical AI solutions and define acceptance criteria. Communicate AI capabilities, limitations, and evaluation results to technical and non-technical stakeholders. Required Technical Skills AI & Generative AI Strong hands-on experience building and deploying production-grade LLM applications. Experience with OpenAI, Anthropic, or Google Gemini APIs. Prompt engineering, structured outputs, JSON schema validation, and function calling. Experience implementing LLM-powered enterprise applications. MCP & AI Agent Development Hands-on experience building and integrating Model Context Protocol (MCP) servers, clients, and tools. Understanding of authentication, access controls, and enterprise system integrations. Experience with LangChain, LangGraph, LlamaIndex, or similar agent orchestration frameworks. Knowledge of multi-step workflows, tool execution, and state management. RAG & Retrieval Experience developing Retrieval-Augmented Generation systems. Knowledge of document processing, chunking, embeddings, and vector databases. Experience with semantic search, hybrid search, and source-grounded responses. Python & Backend Engineering Strong proficiency in Python. Experience with FastAPI or Flask. Knowledge of Pydantic and asynchronous programming. Strong understanding of REST APIs and backend service development. Experience writing clean, modular, maintainable, and production-ready code. AI Evaluation & Engineering Quality Experience creating labeled evaluation datasets and defining evaluation metrics. Knowledge of LLM-as-judge and AI regression testing. Experience with pytest, logging, monitoring, retries, and fallback handling. Understanding of AI observability and production reliability. Preferred Qualifications Experience in finance, financial risk management, SOX compliance, internal audit, or controls automation. Experience processing complex PDFs, scanned documents, tables, and enterprise evidence using OCR or document AI. Familiarity with LLM observability, evaluation frameworks, and production monitoring. Experience with cloud deployment, containers, and CI/CD practices. Experience integrating AI applications with enterprise knowledge repositories, ticketing systems, and compliance platforms. Prior experience at Uber or familiarity with its internal developer ecosystem, data pipelines, and AI platforms. Technology Stack Category Technologies AI & LLM OpenAI, Google Gemini, Anthropic, Prompt Engineering, Structured Outputs Agents & MCP Model Context Protocol, LangChain, LangGraph, LlamaIndex RAG & Retrieval Embeddings, Vector Databases, Semantic Search, Hybrid Search Backend Python, FastAPI, Flask, Pydantic, REST APIs Document Processing pandas, PDF Parsing, OCR, Information Extraction Integrations GRC Platforms, Jira, Google Workspace APIs Testing & Observability pytest, AI Evaluation Frameworks, Agent Tracing, Logging, Monitoring Developer Tools Cursor, Claude Code, GitHub Copilot Qualifications & Experience Minimum 5+ years of professional software engineering experience. Strong hands-on expertise in Python and backend application development. Proven experience building and deploying production-grade LLM-powered applications. Practical experience with MCP servers, clients, and enterprise tool integrations. Strong understanding of RAG architectures, embeddings, vector search, and AI agent orchestration. Experience with FastAPI or Flask, Pydantic, asynchronous programming, and REST APIs. Practical experience with AI evaluation, testing, and production monitoring. Excellent problem-solving, analytical, and debugging skills. Ability to work independently and collaborate effectively with cross-functional teams. Strong communication skills and ability to explain technical concepts to business stakeholders. Position Details Position: Software Engineer II – AI Engineer (Full Stack, Backend-Focused) Experience: 5+ Years Location: Bangalore, Pune Employment Type: Full-time/Contract