Applied AI and Machine Learning Lead
Salary not listed • Jersey City, NJ, United States • Full-time
Posted 16d ago
No salary is listed. Many US states require employers to post a pay range, so ask about pay early.
Remote Work Policy
On-site
Employment Type
Full-time
Categories
AI Engineer
About the job
As an Applied AI and Machine Learning Lead, you will spearhead high-impact generative AI and advanced analytics initiatives, guiding them from research to production. Your role involves leading the design, evaluation, and deployment of foundation-model and agentic AI capabilities aimed at enhancing decision-making, automating tasks, and elevating engineering standards for reliability and governance. You will collaborate closely with product, engineering, and risk stakeholders to establish clear, measurable outcomes and deliver solutions that are scalable, robust, and responsibly implemented.
Responsibilities
- Lead end-to-end generative AI and agentic AI initiatives from problem framing and experimentation through production release and measurable adoption.
- Design and evaluate foundation-model approaches, including model selection, fine-tuning strategies, retrieval and context design, and safety controls.
- Establish rigorous evaluation practices (quality, robustness, latency, cost, and risk) and translate results into clear recommendations and roadmaps.
- Build and operationalize production-grade machine learning and generative AI services using sound engineering practices, monitoring, and incident-ready controls.
- Define and drive semantic consistency across systems by leading semantic modeling standards and lifecycle governance in partnership with domain experts.
- Implement integration patterns that align data pipelines, application interfaces, and user experiences to reduce semantic conflicts and increase trust in outcomes.
- Partner with cross-functional leaders to define success metrics, manage trade-offs, and deliver outcomes aligned to business priorities and oversight expectations.
- Mentor and develop engineers and data scientists through technical leadership, reviews, and coaching to raise team capability and delivery velocity.
- Communicate complex technical concepts to both technical and non-technical audiences, influencing decisions through clarity, evidence, and pragmatism.
Requirements
- Master’s degree in computer science, engineering, statistics, or a related quantitative discipline.
- 7+ years of applied experience building and deploying machine learning and generative AI solutions in production environments.
- Strong proficiency in Python and modern machine learning frameworks (e.g., PyTorch).
- Demonstrated ability to lead experimentation and evaluation, including defining metrics, running controlled comparisons, and documenting results for stakeholders.
- Experience deploying and operating models in production, including performance optimization, monitoring, and reliability trade-offs.
- Working knowledge of responsible AI practices, model risk concepts, and governance controls suitable for regulated environments.
- Strong software engineering fundamentals, including version control, code review, testing, and maintainable design.
- Proven ability to partner across product, engineering, and business stakeholders to translate ambiguous needs into clear outcomes and delivery plans.