Applied AI/ML Lead
Salary not listed • Jersey City, NJ, United States • Full-time
Posted 1d ago
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Job Location
Jersey City, NJ, United States
Tech Stack
Remote Work Policy
On-site
Employment Type
Full-time
Categories
AI Engineer
About the job
As an Applied AI/ML Lead within the Corporate Sector, Trade Surveillance Technology team, you will be responsible for delivering AI/ML enabled analytical and technical solutions that support market surveillance and regulatory compliance capabilities. You will work across the full model lifecycle—from problem framing and data exploration to model development, productionization, and monitoring, partnering closely with data science and engineering teams. You will translate ambiguous business needs into robust, production-grade AI systems while championing sound engineering and responsible AI practices. This is a hands-on technical role with growing scope for technical leadership, mentorship of junior engineers, and influence over architectural decisions.
Responsibilities
- Design, train, evaluate, and fine-tune machine learning, deep learning, and large language models (LLMs) for business use cases.
- Build and maintain scalable ML/Feature/Data pipelines for training, deployment, inference, and monitoring.
- Perform feature engineering, data validation, and preprocessing on large, complex datasets.
- Prototype new AI/ML techniques, including generative AI, RAG, and agentic frameworks.
- Integrate models and AI services into applications via APIs, ensuring performance, latency, and cost efficiency.
- Implement testing, evaluation frameworks, and monitoring for drift, bias, and degradation.
- Adhere to responsible AI and model risk standards.
- Produce technical documentation and provide guidance and code review support to junior team members.
- Lead projects end-to-end from requirements to delivery.
Requirements
- Master's degree in a quantitative field (or equivalent experience) and 6+ years of hands-on experience building and deploying ML/AI models in production.
- Strong programming proficiency in Python, SQL, Relational Databases, and Linux.
- Familiarity with AI/ML frameworks like scikit-learn, PyTorch, and agent building libraries.
- Solid understanding of ML fundamentals, deep learning, model evaluation, and optimization.
- Experience with generative AI/LLMs, including prompt engineering, fine-tuning, embeddings, and RAG.
- Proficiency in MLOps tooling and practices: containerization (Docker), orchestration (Kubernetes), CI/CD, model versioning, and pipeline tools (e.g., MLflow, Kubeflow, Airflow).
- Experience with internal cloud environments (e.g., Gaia, GKP) for AI/ML model deployment.
- Hands-on experience with at least one major cloud platform (AWS, Azure, or GCP) and its ML services.
- Strong data skills: SQL, data pipelines.
- Familiarity with software engineering best practices: version control (Git), testing, code review, and API design.
- Experience with GenAI Gateway services integration and telemetry/eval/cost analysis tools (e.g., Phoenix).