Gen AI

Key Responsibilities:

• Design and implement end-to-end AI solutions (RAG systems, AI agents, copilots, ML pipelines).
• Own featurelevel architecture within larger AI platforms.
• Build and optimize LLMbased workflows: prompt design, embeddings, retrieval, evaluation.
• Integrate AI capabilities into enterprise applications via APIs and microservices.
• Drive POC pilot production transitions with stability and cost controls.
• Implement AI observability: logging, evaluation metrics, hallucination checks, fallbacks.
• Collaborate with Product, Business, Security, and Data teams to shape AI use cases.
• Mentor junior engineers and contribute to coding standards and best practices.
• Follow Responsible AI, security, and data governance guidelines.

Technical Skills Required– 

• Strong handson experience with: 
• Python, REST APIs, microservices
• LLMs, RAG, embeddings, vector databases
• Prompt engineering and evaluation techniques
• Practical experience with: 
• Cloud platforms (Azure/AWS)
• Model inference optimization and cost awareness
• CI/CD, Git, basic MLOps concepts
• Working knowledge of: 
• Data pipelines, unstructured data processing
• AI security, guardrails, and enterprise constraints

Preferred Qualifications

• 5–8 years total engineering experience
• 2–4 years working with AI / ML / GenAI systems
• Proven experience delivering production AI features, not just experiments

Success Metrics

• AI features ship reliably and are adopted by business users.
• POCs convert into stable production deployments.
• Reduced manual effort, faster decisions, or measurable business impact.
• Junior engineers accelerate faster under this role’s guidance.