Generative Ai

Role Summary Design, build, and productionize Applied AI solutions that solve real business problems. This role is hands on, out come driven, and responsible for turning AI concepts into reliable, scalable systems under defined architectural and governance standards.Key Responsibilities:

• Design and implement end to end AI solutions (RAG systems, AI agents, copilots, ML pipelines).
• Own feature level architecture within larger AI platforms.
• Build and optimize LLM based 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

• 6–8 years total engineering experience
• 3–4 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.