Applied AI ML Senior Associate, 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 and optimizations. Collaborate with cross-functional teams to deliver scalable, production-ready AI systems, conduct experiments using the latest ML technologies, and tune models for optimal performance. Own end-to-end code development in Python for both proof-of-concept and production-ready solutions, integrate generative AI within the ML platform, and drive adoption of modern ML infrastructure and best practices. Optimize system accuracy and performance by identifying and resolving inefficiencies, ensure responsible AI practices and model governance with regulatory compliance, and communicate technical concepts and results to both technical and business stakeholders.
Required Qualifications
- Master's or PhD in Computer Science, Engineering, Mathematics, or a related quantitative field.
- Minimum 3 years of hands-on experience in applied machine learning, including generative AI, large language models, or foundation models.
- 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.
- Understanding of prompt engineering, agentic workflows, and orchestration frameworks.
- Experience with cloud platforms (AWS, Azure, GCP) and distributed systems (Kubernetes, Ray, Slurm).
- 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.
- 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.
- Experience with building and deploying ML models on cloud platforms such as AWS Sagemaker, EKS, etc.
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