Senior Product Software Engineer (ML+ AI + Graph DB) at Wolters Kluwer

pune, maharashtra, India -

Full Time

Start Date

Immediate

Expiry Date

17 Oct, 26

Salary

0.0

Posted On

19 Jul, 26

Experience

10 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Large Language Models, Retrieval-Augmented Generation, Agentic AI, Model Context Protocol, Python, Semantic Kernel, LangChain, Vector Databases, Azure OpenAI, Graph Databases, Full Stack Development, Prompt Engineering, Multi-agent Systems, C#, .NET Core

Industry

Software Development

Description

Position Summary We are seeking a highly skilled Senior AI / Full Stack Engineer with deep expertise in modern AI systems and strong hands-on experience in full-stack development. This role focuses on building AI-powered and agentic applications, leveraging LLMs, autonomous agents, and intelligent workflows. You will design and develop systems that incorporate advanced LLM techniques, Retrieval-Augmented Generation (RAG), Model Context Protocol (MCP), and agent-based architectures, enabling intelligent automation and decision-making across applications. Education Bachelor’s degree in Engineering, Computer Science, or equivalent. Must Have : 9+ years of professional software development experience, 3 years of relative experience in building AI product AI / LLM & Agentic Systems Strong understanding of AI/ML concepts with hands-on AI application development Experience working with Large Language Models (LLMs) (Azure OpenAI, OpenAI, Claude APIetc.) LLM & RAG Foundations Deep knowledge of: Prompt engineering & optimization Retrieval-Augmented Generation (RAG) Embeddings and vector databases (Azure AI Search, Pinecone, FAISS, etc.) Tokenization, context handling, hallucination mitigation Agentic AI & MCP Expertise Strong understanding of agent-based architectures (single-agent & multi-agent systems) Experience designing and building autonomous or semi-autonomous AI agents Agent Frameworks Hands-on experience with frameworks such as: Semantic Kernel (preferred for .NET ecosystem) LangChain / LangGraph LlamaIndex / AutoGen (or similar) Agent Capabilities Experience implementing: Tool-using agents (function calling, API integrations) Planning and reasoning workflows (ReAct or similar patterns) Agent orchestration and workflow automation Building or integrating MCP-compatible servers/tools Understanding of: Memory models (short-term, long-term, vector memory) Human-in-the-loop systems AI guardrails, safety, and observability Core Engineering Skills Strong knowledge of multi-threading, scalability, performance, and security Experience with relational databases (SQL Server, PostgreSQL) Experience with cloud platforms (Azure preferred) Knowledge of Azure AI ecosystem (Azure OpenAI, Cognitive Services, AI Search) Experience working in Agile/Scrum environments Strong debugging, problem-solving, and analytical skills Experience with Git and version control systems Experience with Python for AI/ML pipelines Exposure to Graph-based systems / Knowledge Graphs (Neo4j, Cosmos DB, etc.) Experience with model evaluation, monitoring, and prompt/agent testing frameworks semantic search Machine Learning: Recommendation engines, demand forecasting, anomaly detection, clustering, predictive modelling, deep learning, graph-based scoring & community analysis Use of Copilot tools like Codex, Claude, GHCP etc. for software development. Have developed software using SDD with help of SpecKit, OpenSpec etc. Nice to Have Experience with fine-tuning or custom LLM pipelines Experience with multi-agent orchestration frameworks (CrewAI, advanced AutoGen use cases) Frontend experience with ReactJS, JavaScript, HTML5, CSS3 Knowledge of CI/CD and MLOps practices Exposure to multi-modal AI systems Familiarity with MCP ecosystems, tool registries, or emerging AI interoperability standards Experience in C#, .NET Core/.NET Framework Experience with RESTful APIs and distributed systems Essential Duties and Responsibilities Design and develop AI-first and agentic applications using LLMs and modern frameworks Build and optimize RAG pipelines, embeddings, and semantic search systems Design and implement autonomous agents and multi-agent workflows Develop and integrate MCP-based tools and services to enable AI interaction with enterprise systems Integrate AI capabilities into enterprise applications and backend systems Collaborate with architects to build scalable AI-enabled cloud architectures Ensure performance, reliability, safety, and observability of AI systems Guide and mentor developers in AI engineering and agentic design patterns Work closely with product, design, and data teams to deliver AI-driven features Troubleshoot and resolve complex issues across AI and traditional systems Maintain clean, testable, and well-documented code Stay updated with latest advancements in LLMs, agentic AI, MCP, and AI tooling ecosystem What We’re Looking For Engineer who can build production-grade AI systems, not just prototypes Strong understanding of LLMs, agents, and tool integration protocols (like MCP) Ability to design end-to-end intelligent workflows and automation systems Balance of software engineering excellence + AI innovation mindset Our Interview Practices To maintain a fair and genuine hiring process, we kindly ask that all candidates participate in interviews without the assistance of AI tools or external prompts. Our interview process is designed to assess your individual skills, experiences, and communication style. We value authenticity and want to ensure we’re getting to know you—not a digital assistant. To help maintain this integrity, we ask to remove virtual backgrounds and include in-person interviews in our hiring process. Please note that use of AI-generated responses or third-party support during interviews will be grounds for disqualification from the recruitment process. Applicants may be required to appear onsite at a Wolters Kluwer office as part of the recruitment process.

Responsibilities

Design and develop production-grade AI-first applications and autonomous agentic workflows using LLMs and RAG pipelines. Integrate MCP-based tools into enterprise systems and mentor developers on AI engineering design patterns.