What Youll Do

Build AI Products, Not Just Models

- Design and develop GenAI-powered applications using LLMs, RAG, and agentic workflows
- Translate business problems into measurable AI-driven outcomes
- Define evaluation metrics and product success criteria

Engineer Intelligent Systems at Scale

- Build and optimize retrieval-augmented generation (RAG) pipelines
- Develop agent-based AI systems with tool usage and orchestration
- Deploy solutions on cloud-native architectures (Azure preferred)

Own the AI Lifecycle (GenAI Ops)

- Implement prompt engineering strategies, embeddings, and fine-tuning approaches
- Build evaluation frameworks and guardrails for reliability and safety
- Enable continuous monitoring of quality, latency, and cost

Drive Responsible & Secure AI

- Embed responsible AI practices (bias detection, explainability, moderation)
- Ensure data privacy, governance, and compliance standards
- Build systems aligned with enterprise security and trust principles

Collaborate & influence

- Partner with product, data, and engineering teams to prioritize AI features
- Influence enterprise AI architecture and best practices
- Enable teams through reusable AI components and APIs

What You Bring Must-Have Skills

- Strong programming in Python and SQL




- Hands-on experience with GenAI patterns: Prompt engineering, Retrieval-Augmented Generation (RAG), Embeddings & semantic search, Agentic workflows
- Experience with AI/ML frameworks: PyTorch / TensorFlow, Hugging Face, LangChain / Semantic Kernel
- Experience deploying AI solutions on cloud platforms (Azure preferred): Azure OpenAI, Azure ML , Azure AI Search

Engineering & Platform Skills

- Strong foundation in data processing (Pandas, NumPy, Spark/Databricks)
- Experience with MLOps / GenAI Ops: MLflow, pipelines, CI/CD, Docker, Azure DevOps
- Experience building APIs and integrating AI into applications
- Understanding of monitoring, observability, and system reliability

Valuable-to-Have

- Experience building agent-based or autonomous AI systems
- Cost optimization for LLM workloads
- Multi-modal AI (text, image, voice)
- Frontend integration for AI-driven experiences

Qualifications

- Bachelor’s or Master’s in Computer Science, AI/ Data Engineering, or related field
- 2+ years of experience in AI engineering or development

Additional

- Certifications in AI, genAI or Ganetic AI
- Experience with data governance, security, and compliance standards

📌 Technical AI Product Engineer (Bengaluru)
🏢 EY
📍 Bengaluru