AI / Generative AI Engineer – Python, LLMs, RAG, AI Agents, Azure OpenAI & AWS Bedrock

Job Summary

Synechron is seeking an AI / Generative AI Engineer with 6+ years of experience in designing, developing and deploying AI-powered applications. The role will focus on Generative AI, Large Language Models (LLMs), Agentic AI, Retrieval-Augmented Generation (RAG), cloud-based AI platforms and production-grade software engineering.

The position will work with business stakeholders, solution architects, engineering teams, DevOps and MLOps teams to deliver scalable AI solutions that address business needs, integrate with enterprise applications and operate reliably in production environments.

This is a full-time position based in Pune, Bengaluru, Hyderabad or Mumbai , with a hybrid working model. The role contributes to business objectives by accelerating AI adoption, improving automation, enabling intelligent applications and delivering secure, maintainable and measurable AI capabilities.

Software Requirements

Required

• Python: Strong hands-on experience in Python development for AI applications, data processing, model integration and API development; experience with the project-supported version.

• Generative AI and LLMs: Practical experience developing applications using LLMs and foundation models.

• NLP and Transformers: Working knowledge of Natural Language Processing, Transformers, embeddings and prompt engineering.

• RAG: Experience designing and implementing Retrieval-Augmented Generation pipelines.

• Vector Databases: Experience with one or more of the following:PineconeChromaDBFAISSWeaviateEquivalent vector database technologies

• LangChain and LangGraph: Hands-on experience building LLM applications, orchestration workflows or AI agents using current project-supported versions.

• Agentic AI Frameworks: Experience developing intelligent AI agents and agentic workflows.

• Model Context Protocol (MCP): Working knowledge or practical experience applying MCP concepts in AI applications.

• OpenAI / Azure OpenAI: Experience integrating and using OpenAI or Azure OpenAI services.

• AWS Bedrock: Experience using AWS Bedrock or equivalent managed foundation-model services.

• Hugging Face Ecosystem: Familiarity with relevant models, libraries and tools used for Generative AI development.

• Cloud Platforms: Hands-on experience with Azure and/or AWS.

• REST APIs and FastAPI: Experience designing or integrating REST APIs and developing AI services using FastAPI.

• Docker and Kubernetes: Experience containerizing and deploying AI applications and services.

• CI/CD Pipelines: Experience supporting automated build, test and deployment pipelines.

• Git and GitHub/GitLab: Experience with source control, branching, code review and collaborative development.

• SQL and NoSQL Databases: Experience working with structured and unstructured data stores.

• Data Pipelines: Experience with data ingestion, preparation and processing pipelines.

• MLOps: Experience with model deployment, monitoring and machine learning lifecycle management.

• AI Guardrails and Responsible AI: Understanding of guardrails, governance, safety, monitoring and responsible use of AI.

Preferred

• Experience delivering enterprise-scale Generative AI solutions.

• Experience with Copilot solutions, AI agents and multi-agent systems.

• Exposure to the BFSI domain .

• Experience with React, Node.js or full-stack development.

• Understanding of security, compliance and governance requirements for AI applications.

• Experience with knowledge graphs and semantic search solutions.

• Experience with multimodal AI applications.

• Experience with fine-tuning strategies and LLM evaluation frameworks.

Overall Responsibilities

• Design, develop and deploy Generative AI solutions using LLMs and foundation models.

• Build end-to-end AI applications covering data ingestion, prompt engineering, RAG pipelines, model orchestration and API integration.

• Develop intelligent AI agents and agentic workflows using LangChain, LangGraph, MCP and other suitable orchestration frameworks.

• Implement AI capabilities using Azure OpenAI, AWS Bedrock, OpenAI APIs and related AI services.

• Design, configure and manage vector databases and semantic search solutions.

• Create scalable APIs and microservices that integrate AI capabilities into enterprise applications.

• Optimize LLM performance through prompt engineering, fine-tuning strategies, retrieval optimization and evaluation frameworks.

• Establish appropriate methods for measuring response quality, relevance, accuracy, latency, reliability and cost.

• Implement AI guardrails, responsible AI practices, monitoring and governance mechanisms.

• Collaborate with DevOps and MLOps teams on deployment, monitoring, model lifecycle management and production support.

• Apply software engineering practices including version control, code reviews, automated testing, documentation and maintainable architecture.

• Work with business stakeholders and solution architects to understand requirements and translate them into practical AI solutions.

• Assess technical feasibility, integration dependencies, data requirements, risks and operational considerations.

• Stay current with developments in Generative AI, Agentic AI, multimodal AI and LLM ecosystems.

• Support sustainable AI engineering by considering model efficiency, resource utilization, infrastructure cost, reuse and long-term maintainability.

• Deliver production-grade AI solutions that meet agreed functional, security, scalability, reliability and support expectations.

Technical Skills (By Category)

Programming Languages

Essential

• Strong Python development skills.

• Ability to write modular, testable, maintainable and production-ready code.

• Ability to develop AI application logic, data-processing components, API services and integration utilities.

Preferred

• JavaScript or TypeScript experience for AI application integration or full-stack development.

• Node.js experience for backend services.

• React experience for developing or integrating AI-enabled user interfaces.

Databases and Data Management

Essential

• Experience with SQL and NoSQL databases.

• Experience designing and supporting data ingestion and processing pipelines.

• Understanding of structured, unstructured and semi-structured data.

• Experience with vector databases, embeddings, indexing and similarity search.

• Understanding of knowledge graph and semantic search concepts.

• Ability to assess data quality, data access, data lineage and data relevance for AI applications.

Preferred

• Experience with large-scale data processing architectures.

• Experience integrating knowledge graphs with RAG or semantic search solutions.

• Experience optimizing vector search performance and retrieval quality.

• Experience with data governance and metadata management.

Cloud Technologies

Essential

• Hands-on experience with Azure and/or AWS cloud platforms.

• Practical experience using Azure OpenAI, AWS Bedrock or related cloud AI services.

• Understanding of cloud-based deployment, scalability, availability, monitoring and access control.

• Ability to integrate cloud AI services with APIs, databases and enterprise applications.

Preferred

• Experience designing enterprise-scale AI platforms on cloud infrastructure.

• Experience with cloud-based model monitoring, managed AI services and infrastructure automation.

• Experience optimizing cloud resource usage and AI application costs.

Frameworks and Libraries

Essential

• LangChain and LangGraph for LLM application development and orchestration.

• Agentic AI frameworks for AI-agent and agentic workflow development.

• OpenAI and/or Azure OpenAI integration.

• AWS Bedrock integration.

• Hugging Face ecosystem.

• FastAPI for AI service and REST API development.

• RAG architectures, prompt engineering, Transformers and embeddings.

• Experience with LLM-based applications and foundation models.