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.
• Solid 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

📌 Software Engineer II – AI Engineer (Bengaluru)
🏢 LogicHive®
📍 Bengaluru