Applied AI ML Lead, Chief Data & Analytics Office
On-siteJersey City, New Jersey, United States
Job Summary
Lead the hands-on design, development, and deployment of advanced AI, GenAI, and large language model solutions while serving as a subject matter expert on machine learning techniques. Collaborate with cross-functional teams to deliver scalable, production-ready systems, own end-to-end Python code development, and integrate generative AI within the ML platform using state-of-the-art techniques. Conduct experiments, tune models for optimal performance, and optimize system accuracy by resolving inefficiencies. Ensure responsible AI practices, model governance, and compliance with regulatory standards while mentoring other engineers and scientists. Drive adoption of modern ML infrastructure and tools to address complex business challenges across multiple domains.
Required Qualifications
- Master's or PhD in Computer Science, Engineering, Mathematics, or a related quantitative field
- Minimum 8 years of hands-on experience in applied machine learning, including generative AI, large language models, or foundation models
- At least 5 years of experience programming in Python; experience with ML frameworks such as PyTorch or TensorFlow
- Proven experience designing, training, and deploying large-scale ML/AI models in production environments
- Deep understanding of prompt engineering, agentic workflows, and orchestration frameworks
- Experience with cloud platforms (AWS, Azure, GCP) and distributed systems (Kubernetes, Ray, Slurm)
- Solid grasp of MLOps tools and practices (MLflow, model monitoring, CI/CD for ML)
- Strong communication skills with the ability to explain complex technical concepts to diverse audiences
- Demonstrated leadership in working effectively with engineers, product managers, and other ML practitioners
- Experience applying data science and ML techniques to solve business problems
- Passion for detail, follow-through, and technical excellence
Desired Qualifications
- Experience with high-performance computing and GPU infrastructure (e.g., NVIDIA DCGM, Triton Inference)
- Familiarity with big data processing tools and cloud data services
- Advanced knowledge in reinforcement learning, meta learning, or related advanced ML areas
- Experience with search/ranking, recommender systems, or graph techniques
- Background in financial services or regulated industries
- Experience with building and deploying ML models on cloud platforms such as AWS Sagemaker, EKS, etc.
- Published research or contributions to open-source GenAI/LLM projects
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