Applied AI/ML Sr. Associate - Small Business
About The Position
Chase Small Business Banking serves 7 million small business clients with financial services including lending, deposits, cards, and online solutions. The Senior Associate Applied AI/ML Scientist will develop and operate agentic AI products supporting Small Business Lending, partnering with Product and Engineering to turn business needs into production-ready systems. The role owns prompts, tool usage, memory, routing, and evaluations that measure task success, safety, and cost, and helps maintain a continuous learning loop from production traces through failure analysis, evaluation cases, and verified fixes. The position involves designing and deploying machine learning and generative AI solutions; building agentic systems; evaluating performance; fine-tuning models; implementing safety guardrails; and integrating data, retrieval, and MLOps practices. It also requires presenting findings to technical and non-technical stakeholders, including senior leaders.
Requirements
- Bachelor’s, master’s, or PhD degree in computer science or a data science-related discipline, with 3+ years of industry experience.
- Strong software engineering experience contributing to production systems.
- Proficiency in Python, including asynchronous programming and API development with FastAPI, Flask, or similar frameworks.
- Hands-on experience building and deploying generative AI or LLM applications in production.
- Hands-on experience with LLM orchestration and agentic AI frameworks such as LangChain, LangGraph, or similar tools.
- Experience implementing evaluations and guardrails using tools such as deepeval or similar libraries.
- Experience using tracing and observability tools such as Arize Phoenix.
- Familiarity with machine learning frameworks such as PyTorch or scikit-learn, including adapting and fine-tuning models.
- Experience integrating vector databases and search or indexing systems into AI applications.
- Working knowledge of MLOps, including CI/CD, monitoring, and lifecycle management.
- Strong problem-solving and communication skills, with the ability to work with technical and non-technical stakeholders.
Responsibilities
- Design, build, and deploy machine learning and generative AI solutions to address business needs.
- Build agentic AI systems using LLM orchestration frameworks such as LangChain, LangGraph, or similar tools.
- Evaluate agent performance using golden and adversarial test sets, LLM-judge rubrics, and evaluation libraries.
- Fine-tune small language models and open-weight models for custom objectives.
- Implement and tune safety guardrails, including input/output checks, prompt-injection defenses, PII handling, and spend limits; monitor effectiveness and false-positive impact.
- Partner with data scientists, product teams, and business stakeholders to turn requirements into scalable AI/ML solutions.
- Apply MLOps practices such as CI/CD, versioning, observability, and model and prompt lifecycle management.
- Work with structured and unstructured data, integrating relational and vector databases to support retrieval-augmented generation (RAG).
- Present findings clearly to technical and non-technical stakeholders, including senior leaders.
- Maintain a continuous learning loop from production traces through failure analysis, evaluation cases, and verified fixes.
Benefits
- Competitive total rewards package, including base salary determined by role, experience, skill set, and location.
- Eligible roles may receive commission-based pay and/or discretionary incentive compensation in cash and/or forfeitable equity.
- Comprehensive health care coverage.
- On-site health and wellness centers.
- Retirement savings plan.
- Backup childcare.
- Tuition reimbursement.
- Mental health support.
- Financial coaching.
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What This Job Offers
Job Type
Full-time
Career Level
Senior
