Example Company
Software engineer
- Location
- San Francisco
- Type
- Full time
- Compensation
- $150k-$200k
- Posted
- 2 days ago
Join a team building useful software. Work closely with engineering and design to turn new ideas into products.
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Applied AI/ML Sr. Associate - Small Business
- Location
- New York
- Type
- Full time
- Posted
- 8D AGO
About the role
Chase Small Business Banking is a world-class brand serving 7 million small business clients with a full suite of financial services, including lending, deposits, cards, and online solutions.
As a Senior Associate Applied AI/ML Scientist, you will develop and operate agentic AI products supporting Small Business Lending. You will partner with Product and Engineering to translate business needs into production-ready systems - you'll build and own the prompts, tool usage, memory, and routing that shape agent behavior, and build evaluations that show whether each change improves task success, safety, and cost. You will help maintain the continuous learning loop from production traces to failure analysis, evaluation cases, and verified fixes.
Job Responsibilities
- Design, build, and deploy machine learning and generative AI solutions to address business needs.
- Build agentic AI systems with LLM orchestration frameworks such as LangChain, LangGraph, or similar tools.
- Evaluate agent performance with golden and adversarial test sets, LLM-judge rubrics, and evaluation libraries.
- Fine-tune SLMs 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.
Required Qualifications, Capabilities, and Skills
- Bacholer’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.
- Proficient 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 including deepeval and similar libraries.
- Experience using tracing observability tools such as Arize Phoenix.
- Familiarity with machine learning frameworks such as PyTorch or scikit-learn, including adapting and fine-tuning of 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.
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