Design, prototype, validate, and productionize traditional ML models and GenAI/LLM-based solutions (including RAG and agentic workflows) to meet business objectives across credit products.
Translate business problems into technical solutions: define metrics, success criteria, evaluation strategy, and experimentation plans for both ML models and GenAI experiences.
Own end-to-end model/system lifecycle for assigned use cases, including data ingestion and lineage, feature engineering, model development, evaluation, deployment, monitoring, and retraining/re-optimization.
Build and operationalize AI agents that automate business processes and augment agent-assist workflows, including:
tool-using agent patterns, multi-step orchestration
memory/state management and safe handoffs to humans
secure connector design to upstream/downstream systems
integration into APIs, microservices, or agent frameworks
Ensure explainability, fairness, and regulatory compliance for credit/financial use cases. Produce required documentation and artifacts for model risk and audit.
Integrate solutions into production by collaborating with software engineers and MLOps teams (e.g., model/LLM services, RAG services, agent runtimes) and supporting CI/CD for ML/GenAI components.
Instrument monitoring and observability across the full lifecycle:
traditional ML: performance, drift, data quality, latency
GenAI/agents: quality/safety metrics, grounding/citation quality, tool-call reliability, latency/cost, and drift in retrieval inputs
define retraining and incident playbooks, and lead mitigation/rollback actions.
Build robust evaluation pipelines for both paradigms:
holdout strategies, cross-validation, backtesting
uplift/A-B testing frameworks and business impact estimation
scenario-based and simulation-based testing for agent behavior and GenAI responses.
Drive responsible AI and safety for agents/LLMs, including guardrails (authorization/scope limits, confirmation flows, human-in-the-loop escalation), bias mitigation, and privacy-by-design (PII handling).
Communicate clearly with non-technical stakeholders and senior leadership—articulating model/system behavior, limitations, risks, and measurable outcomes.
Mentor and raise team capability in reproducible ML engineering and agent/GenAI development practices.
Coordinate with data engineering to ensure reliable, documented data sources and lineage.
Keep abreast of emerging AI safety practices and recommend improvements to guardrails and SDLC processes.

Requirements

Bachelor's degree in Computer Science or a related field (e.g., Machine Learning, Statistics, Data Science, Electrical/Computer Engineering) or equivalent practical experience
5+ years of applied ML/AI experience (or equivalent) with demonstrated hands-on delivery in production environments.
Strong software engineering skills in Python and modern engineering practices (testing, modular design, reliable pipelines, maintainability).
Production ML and MLOps experience: Docker, CI/CD, model serving, monitoring/observability, and experience working with orchestrated pipelines (e.g., Kubernetes and/or equivalent).
Strong data skills: advanced SQL and experience with big data platforms (e.g., BigQuery, Spark, Databricks, Snowflake) and/or streaming/event-driven systems.
Traditional ML expertise: practical experience building and productionizing models for credit use cases (e.g., classification, regression, time-series forecasting, anomaly detection, graph-based fraud approaches).
GenAI/LLM expertise: hands-on experience building and evaluating LLM-based systems, including RAG, prompt engineering, evaluation of response quality/grounding, and conversational AI/agent-assist applications.

Benefits

Immediate medical, dental, vision and prescription drug coverage
Flexible family care days, paid parental leave, new parent ramp-up programs, subsidized back-up child care and more
Family building benefits including adoption and surrogacy expense reimbursement, fertility treatments, and more
Vehicle discount program for employees and family members and management leases
Tuition assistance
Established and active employee resource groups
Paid time off for individual and team community service
A generous schedule of paid holidays, including the week between Christmas and New Year’s Day
Paid time off and the option to purchase additional vacation time.

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills & Tools

machine learningartificial intelligencePythonSQLDockerKubernetesbig dataRAGprompt engineeringmodel evaluation

Soft Skills

communicationmentoringcollaborationproblem-solvingleadership

Certifications

Bachelor's degree in Computer ScienceMachine LearningData ScienceElectrical Engineering