Software Engineering II-SUPPORT SERVICES-Data & Analytics - In House Engineering

Job Description: AI Engineer – LLM & Enterprise AI Solutions (6–8 Years)

Location

Bengaluru / Hybrid

Experience

6–8 years (with 3+ years in AI/ML and at least 1–2 years in GenAI/LLM-based solutions)

Role Overview

We are seeking an experienced AI Engineer to partner with Data Scientists and build production-grade AI solutions leveraging Large Language Models (LLMs) and enterprise knowledge bases. This role focuses on bridging research and engineering by converting models and prototypes into scalable, secure, and reliable applications following full SDLC practices.

Key Responsibilities

• Collaborate closely with Data Scientists to operationalize ML/AI models into production systems
• Design and develop LLM-powered applications such as copilots, chatbots, and knowledge assistants
• Build Retrieval-Augmented Generation (RAG) pipelines using enterprise data
• Integrate AI solutions with knowledge bases, document stores, and vector databases
• Develop end-to-end pipelines for ingestion, embedding, retrieval, and inference
• Ensure adherence to SDLC processes including testing, deployment, and monitoring
• Deploy solutions on cloud platforms with focus on scalability and reliability
• Implement MLOps practices including CI/CD, monitoring, and retraining
• Ensure security, governance, and responsible AI practices

Required Skills

• Hands-on experience with LLMs (OpenAI, Azure OpenAI, Hugging Face)
• Strong understanding of RAG, embeddings, prompt engineering
• Proficiency in Python / Java and API development
• Experience with Java Spring boot / FastAPI/ Flask and microservices
• Knowledge of vector databases like FAISS or Pinecone
• Experience in SQL, big data technologies (Spark, Redshift), complex data processing, and data modeling.
• Cloud deployment experience (Azure/AWS/GCP)
• Familiarity with Docker, Kubernetes, CI/CD

Preferred Qualifications

• Experience in enterprise environments
• Knowledge of LangChain, LlamaIndex, Semantic Kernel
• Understanding of model monitoring and observability
• Awareness of security, compliance, and data privacy

Education

Bachelor’s or Master’s in Computer Science, Data Science, AI, or related field