Senior AI Engineer – AI/ML + .NET

Senior AI Engineer – AI/ML + .NET

Experience: 7–10 Years
Employment Type: C2C / C2H / FTE
Location: Remote – Pan India
Working Hours: 11:00 AM – 9:00 PM
Role Focus: 70% AI/GenAI | 30% .NET

Job Summary

We are looking for a Senior AI Engineer – AI/ML + .NET with strong hands-on experience in Generative AI, Machine Learning, LLMs, RAG, MCP, AI Agents, Python, and Microsoft .NET technologies.

Key Responsibilities
Generative AI & AI Engineering – 70%

• Design, develop, test, and deploy enterprise-grade Generative AI solutions.
• Build applications using LLMs, RAG architectures, AI Agents, MCP, and Agentic AI patterns.
• Develop AI agents capable of reasoning, tool calling, orchestration, and enterprise workflow automation.
• Design and implement complete RAG pipelines, including:
• Document ingestion
• Chunking
• Embeddings
• Vector search
• Retrieval
• Contextual generation
• Integrate Azure OpenAI and Azure AI Services into enterprise applications.
• Apply prompt engineering and context engineering techniques.
• Work with vector databases, semantic search, and AI/LLM orchestration frameworks.
• Develop scalable AI/ML services and integrate them with enterprise APIs and systems.
• Evaluate AI solutions based on accuracy, relevance, latency, reliability, scalability, and production readiness.
• Identify and implement AI/GenAI use cases that improve enterprise and healthcare workflows.
• Build reusable AI components, APIs, agents, frameworks, and services for enterprise adoption.
• Collaborate with architects, product teams, business stakeholders, and engineering teams to convert AI requirements into technical solutions.
• Implement responsible AI practices including guardrails, evaluation, security, and reliability where applicable.

.NET & Enterprise Application Development – 30%

• Design and develop enterprise applications using C# and .NET Core.
• Build scalable REST APIs and microservices using ASP.NET Core.
• Integrate AI/GenAI capabilities into existing .NET enterprise applications.
• Develop backend services following clean architecture, SOLID principles, and industry best practices.
• Design secure and scalable application integrations.
• Optimize SQL Server queries, database operations, and application performance.
• Participate in technical design, architecture discussions, code reviews, debugging, and development activities.
• Work closely with AI and software engineering teams to integrate AI services into enterprise platforms.

DevOps & Engineering Practices

• Use Git for source control, branching, and collaborative development.
• Develop and maintain CI/CD pipelines for application and AI workloads.
• Follow Agile/Scrum development methodologies.
• Participate in sprint planning, daily stand-ups, retrospectives, and technical discussions.
• Implement automated testing, documentation, code quality, and production-readiness practices.
• Troubleshoot AI services and application issues and perform root-cause analysis.
• Monitor application and AI solution performance and reliability.
• Mentor junior developers and contribute to engineering standards and best practices.

Mandatory SkillsAI / Generative AI

• AI/ML
• Generative AI / GenAI
• Large Language Models (LLMs)
• RAG – Retrieval-Augmented Generation
• MCP – Model Context Protocol
• AI Agent Development / Agentic AI
• Prompt Engineering / Context Engineering
• Vector Databases
• Semantic Search
• AI/LLM Orchestration
• Production implementation of GenAI solutions
• Python

.NET & Backend

• C#
• .NET Core
• ASP.NET Core
• REST APIs
• Microservices
• SQL Server
• Enterprise application development
• API integration

Cloud & DevOps

• Azure OpenAI
• Azure AI Services
• Git
• CI/CD
• Agile/Scrum
• Cloud-based application development

Good-to-Have Skills

• Angular
• React
• Healthcare AI / Healthcare domain experience
• Microsoft Azure services
• Azure AI Foundry
• LangChain
• LangGraph
• Semantic Kernel
• AutoGen
• Azure AI Search
• Pinecone
• Weaviate or other vector databases
• Multi-agent architectures
• AI evaluation and guardrails
• Responsible AI
• Docker
• Kubernetes
• MLOps / LLMOps
• Event-driven architecture
• Messaging platforms

Experience & Qualifications

• 7–10 years of overall software development experience.
• 2–4+ years of hands-on experience in…