Data & AI Engineer — Production‑Grade Analytics

Bank of America; Charlotte (NC); On-site; USD 120,000 - 190,000; Full time

Job summary

Bank of America is seeking a Data Analyst / AI Engineer to design, build and operationalise quantitative models and agentic AI solutions for complex risk and data-management use cases. The role combines Python data engineering with governance, provenance and production deployment in a highly regulated setting.

The successful candidate will translate business objectives into scalable data and AI products, collaborating with risk experts, data owners and tech teams to deliver explainable,

Qualifications

• Bachelor's or Master's degree in Computer Science, Data Science, Machine Learning, Engineering, Statistics, Mathematics or a related quantitative discipline.
• 5+ years of professional experience building data analytics, machine learning or AI solutions, with demonstrated ownership of solutions beyond experimentation and into production.
• Advanced hands-on Python development, including clean, testable and modular code; experience with pandas, NumPy, scikit-learn and at least one deep-learning framework such as PyTorch or TensorFlow.
• Practical knowledge of supervised and unsupervised learning, model selection, benchmarking, error analysis and explain ability.
• Hands-on experience building and deploying generative AI or agentic AI solutions using LLMs, retrieval-augmented generation, embeddings, vector search, tool calling, prompt management, multi-step orchestration using LangGraph and MCP.

Responsibilities

• Build agentic AI solutions that use large language models, retrieval-augmented generation, tools, APIs and workflow orchestration to support research, analysis, decision support and operational processes.
• Develop robust Python services, reusable libraries, data pipelines and APIs that integrate structured and unstructured data from multiple Systems of Record.
• Create evaluation frameworks for ML and generative AI solutions, including accuracy, precision/recall, robustness, latency, cost, hallucination, groundedness and task-completion measures.
• Implement appropriate human-in-the-loop controls, guardrails, audit trails, access controls, fallback paths and approval checkpoints for agentic workflows.
• Partner with business and risk stakeholders to frame problems, define measurable success criteria and translate prototypes into scalable, production-grade capabilities.
• Apply data strategy principles to identify authoritative sources, promote reuse, minimise redundant sourcing and align solutions to target-state data architecture.
• Document and maintain end-to-end data lineage from source systems through transformations, features, models, prompts, agent actions and downstream outputs.
• Embed data governance requirements into solution design, including metadata, ownership, classifications, quality controls, retention, privacy, entitlements and issue management.
• Perform data discovery, profiling, quality assessment and root-cause analysis across large, complex datasets; establish controls and metrics to monitor data health.
• Collaborate in cross-functional Agile delivery teams and communicate technical concepts, trade-offs, risks and outcomes clearly to both technical and executive audiences.
• Mentor team members, contribute reusable patterns and standards, and help advance engineering discipline across GRM data and AI initiatives.

Skills

Python development

Data analytics

Machine learning

SQL

Spark

Airflow

Databricks

REST APIs

Git

Docker

CI/CD

Data governance

Data lineage

Education

Bachelor's or Master's degree in Computer Science, Data Science, ML, Engineering, Statistics, Mathematics

Tools

SQL

Spark

Airflow

Databricks

REST APIs

Git

Docker

CI/CD

Job description

Bank of America is seeking a Data Analyst / AI Engineer to design, build and operationalise quantitative models and agentic AI solutions for complex risk and data-management use cases. The role combines Python data engineering with governance, provenance and production deployment in a highly regulated setting.

The successful candidate will translate business objectives into scalable data and AI products, collaborating with risk experts, data owners and tech teams to deliver explainable,