End-to-End AI Product Engineering: Lead the lifecycle of advanced AI products—from architectural design and model selection/fine-tuning to production deployment, monitoring, and performance optimization.
Intelligent Automation & Modernization Solutions: Design and implement intelligent systems that parse, translate, and modernize complex legacy codebases and technical documentation using state-of-the-art Natural Language Processing (NLP) and Large Language Models (LLMs).
Prompt Engineering & Model Fine-Tuning: Develop robust prompt architectures, retrieval-augmented generation (RAG) pipelines, and fine-tuned models to automate domain-specific artifact generation and technical decision-making from high-level user prompts.
Cloud Architecture & Scalability: Leverage Google Cloud Platform (GCP) infrastructure to build resilient, serverless, and scalable AI microservices and batch processing pipelines.
Cross-Functional Collaboration: Partner closely with Product Managers, UX Designers, Software Architects, and Domain Experts to ensure technical feasibility, clear system requirements, and frictionless integration into enterprise ecosystems.
Code Quality & Best Practices: Maintain high engineering standards by establishing CI/CD pipelines for AI assets, automated testing frameworks, robust API design, and comprehensive technical documentation.
Advocacy & Mentorship: Drive an innovation-first culture across the engineering lab, staying ahead of emerging Generative AI/ML research and mentoring junior team members on production ML engineering best practices.
Cloud Infrastructure & AI FinOps: Architect resilient, serverless, and scalable AI microservices on Google Cloud Platform (GCP) while implementing granular tagging, billing telemetry, and cost-attribution frameworks for all AI workloads.
Cost Tracking & Optimization: Monitor, analyze, and optimize model inference costs (token-based API spend, vector database queries, GPU/TPU utilization) to maintain full visibility into product operational costs.