Lead AI/ML Engineer

Architect and deliver enterprise-grade Generative AI solutions, converting complex business challenges into scalable AI powered products. Design advanced systems using Large Language Models (LLMs), Retrieval Augmented Generation (RAG), AI Agents, and multi agent orchestration frameworks such as LangGraph, CrewAI, and Model Context Protocol (MCP). Build cloud-native AI platforms that integrate foundation models, vector databases, and knowledge retrieval pipelines for high availability and performance. Partner with business stakeholders and engineering leadership to define AI roadmaps and prioritize high impact automation opportunities. Establish governance, security, observability, and evaluation frameworks that ensure responsible and reliable deployment of Generative AI applications. Guide technical teams through the full lifecycle of building, deploying, and optimizing production ready AI solutions, balancing scalability with long term maintainability. Set organizational standards for prompt engineering, model evaluation, fine tuning, and LLM application development. Direct MLOps and LLMOps initiatives that enable continuous integration, monitoring, and lifecycle management of AI systems in production. Assess emerging foundation models and agentic frameworks to drive innovation and sustain technological leadership. Mentor AI engineers and architects while engaging directly with clients and executives to accelerate digital transformation.

Developed and deployed machine learning solutions addressing complex problems across diverse business domains. Created predictive models and deep learning applications using Python, TensorFlow, PyTorch, and Scikit-learn. Created end-to-end ML pipelines spanning data preprocessing, feature engineering, model training, and deployment. Delivered Computer Vision and Natural Language Processing (NLP) solutions that improved automation, accuracy, and operational efficiency. Collaborated with cross-functional teams to translate business requirements into scalable, AI-driven solutions. Integrated trained ML components into backend services and APIs, making AI features accessible within real-world software applications. Refined model performance through systematic experimentation, validation, and continuous improvement. Mentored junior engineers and helped establish machine learning best practices and technical standards.

Implemented data processing pipelines and model training workflows to support machine learning initiatives. Built and validated early-stage predictive models using Python and core machine learning libraries. Performed data collection, cleaning, and feature engineering to enhance model accuracy and data quality. Contributed to NLP and foundational computer vision models for proof-of-concept and experimental projects. Collaborated with senior engineers to integrate machine learning components into broader software applications. Participated in model evaluation and debugging to improve reliability and overall performance. Supported backend development tasks underpinning ML workflows and application integration.