Sr AI Engineer
Tech Mahindra
2 - 5 years
Bengaluru
Posted: 20/03/2026
Job Description
AI Application Development
Design and develop AI-powered applications using Generative AI and machine learning models.
Integrate Large Language Models (LLMs) such as OpenAI, Azure OpenAI, or other foundation models into enterprise applications.
Build AI copilots, chatbots, document intelligence solutions, and recommendation systems.
RAG & AI Architecture
Implement Retrieval Augmented Generation (RAG) architectures using vector databases.
Develop pipelines for embedding generation, semantic search, and contextual retrieval.
Optimize prompts, context handling, and response accuracy.
Backend Engineering (.NET)
Build scalable microservices and REST APIs using .NET Core / .NET 8+.
Integrate AI models into backend systems and enterprise platforms.
Implement asynchronous and event-driven processing for AI workloads.
Data & AI Pipelines
Build pipelines for data ingestion, preprocessing, and model inference.
Work with structured and unstructured data including documents, text, and knowledge bases.
AI Deployment & Operations
Deploy AI models and services using containerization and cloud platforms.
Implement monitoring, evaluation, and feedback loops for AI systems.
Ensure security, scalability, and performance of AI services.
Required Skills
Programming & Development
Strong experience in C# / .NET Core / .NET 8
Experience building scalable APIs and microservices
Familiarity with Python for AI development
Generative AI
Experience working with LLMs (OpenAI, Azure OpenAI, Claude, etc.)
Knowledge of Prompt Engineering
Experience implementing RAG architectures
Experience with embeddings and semantic search
AI Frameworks & Tools
Familiarity with LangChain, Semantic Kernel, or similar AI orchestration frameworks
Experience with vector databases (Pinecone, Weaviate, Azure AI Search, etc.)
Knowledge of ML frameworks such as PyTorch, TensorFlow, or Scikit-learn
Cloud & DevOps
Experience with Azure / AWS / GCP
Experience with Docker and CI/CD pipelines
Understanding of MLOps practices
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