Ai Ml Manager

Role Overview

This role leads the design, development, and productionization of ML and Generative AI solutions from concept to deployment. You will architect scalable, reliable, and cost-effective systems, including RAG pipelines, agentic workflows, and model fine-tuning strategies. The role blends deep machine learning expertise with solid software engineering practices to deliver secure, high-performance applications. You will mentor a team, collaborate with cross-functional stakeholders, and ensure models, prompts, and pipelines meet business goals and production standards.

Required Qualifications

• 8-12 years overall industry experience with 8+ years in software engineering building secure, scalable, performant applications
• 5+ years designing, building, and deploying ML/AI applications into production
• 2+ years leading and mentoring ML/Data Science teams (4+ members)
• Hands-on experience with Generative AI/LLMs, prompt engineering, RAG or LLM fine-tuning
• Experience in one or more: document AI, conversational AI, vision AI, NLP
• Ability to evaluate models, tune/optimize performance, and operationalize ML systems
• Strong Python skills with NumPy, Pandas, and ML/DL libraries: Scikit-learn, XGBoost, TensorFlow, PyTorch, LangChain
• Experience building/deploying scalable ML pipelines, including data processing, vector search, model evaluation, and inference serving
• Cloud experience: GCP or equivalent (AWS/Azure) and associated ML services
• Proficiency across data preparation/EDA, handling outliers/imbalance, feature engineering/selection, model selection/training/deployment
• Excellent communication, collaboration, and problem-solving skills

Responsibilities

• Own end-to-end ML/GenAI solution deliveryfrom discovery and design to deployment and operations
• Architect scalable, reliable, and cost-effective Generative AI systems (RAG pipelines, agentic workflows, fine-tuning strategies)
• Design, develop, fine-tune, and operationalize ML/LLM models; evaluate performance and iterate
• Create and maintain prompts and prompt pipelines; analyze and preprocess datasets to improve LLM responses
• Conduct thorough LLM evaluation and model monitoring; iteratively refine prompts and models for accuracy and relevance
• Build and deploy ML pipelines on GCP or equivalent cloud; integrate with data warehouses, ML platforms, dashboards/CRM
• Implement core components for prompt engineering, vector search, data processing, model evaluation, and inference serving
• Write clean, high-quality, production-grade code and enforce security, scalability, and performance standards
• Lead and mentor a team of ML/DS engineers; conduct reviews and guide best practices
• Collaborate with engineers and stakeholders; manage timelines, risks, and successful project delivery