- Problem Definition & Requirements
1.Translate business use cases into detailed AI/ML problem statements and success metrics.
2.Gather and document functional and non-functional requirements, ensuring traceability throughout the development lifecycle.
- Architecture & Prototyping
1.Design end-to-end architectures for GenAI and LLM solutions, including context orchestration, memory modules, and tool integrations.
2.Build rapid prototypes to validate feasibility, iterate on model choices, and benchmark different frameworks and vendors.
- Development & Productionization
1.Write clean, maintainable code in Python, Java, or Go, following software engineering best practices.
2.Implement automated testing (unit, integration, and performance tests) and CI/CD pipelines for seamless deployments.
3.Optimize model inference performance and scale services using containerization (Docker) and orchestration (Kubernetes).
- Post-Deployment Monitoring
1.Define and implement monitoring dashboards and alerting for model drift, latency, and throughput.
2.Conduct regular performance tuning and cost analysis to maintain operational efficiency.
- Mentorship & Collaboration
1.Mentor SDE-1/SDE-2 engineers and interns, providing technical guidance and career development support.
2.Lead design discussions, pair-programming sessions, and brown-bag talks on emerging AI/ML topics.
3.Work cross-functionally with product, QA, data engineering, and DevOps to align on delivery timelines and quality goals.