Data Engineer – Python/AI

Bank of America•Charlotte, NC

1d•Onsite

About The Position

At Bank of America, we are guided by a common purpose to help make financial lives better through the power of every connection. We do this by driving Responsible Growth and delivering for our clients, teammates, communities and shareholders every day. Being a Great Place to Work and providing a culture of caring is core to how we drive Responsible Growth. We are intentional about fostering an inclusive workplace where every teammate has the opportunity to succeed, build a career and contribute to our shared success. This includes attracting and developing exceptional talent, recognizing and rewarding performance, and supporting our teammates’ physical, emotional, and financial wellness through affordable, competitive and flexible benefits. We value the unique perspectives individuals bring from all backgrounds and career paths - whether shaped by military service, community college education, or a wide range of work and life experiences. These journeys foster resilience, leadership and innovation, strengthening our workforce and positively impact the communities we serve. Bank of America is committed to an in-office culture that supports collaboration, engagement, and career development. Our approach includes clear in-office expectations, while providing an appropriate level of flexibility based on role-specific responsibilities and business needs. At Bank of America, you can build a successful career with opportunities to learn, grow, and make an impact. Join us! This job is responsible for developing and delivering data solutions to accomplish technology and business goals and initiatives. Key responsibilities include performing code design and delivery tasks associated with the integration, cleaning, transformation, and control of data in operational and analytical data systems. Job expectations include working with stakeholders and Product and Software Engineering teams to aid with implementing data requirements, analyzing performance, and researching and troubleshooting data problems within system engineering domains. Join a high‑impact technology team within Global Commercial Lending, focused on transforming core lending and payments BAU processes through AI, ML, and Generative AI solutions. This role offers a unique opportunity to design and productionize AI‑driven capabilities that deliver measurable efficiency gains, improved operational resilience, and smarter decisioning across large‑scale enterprise lending platforms. You will work closely with product, operations, and engineering teams to build, deploy, and scale ML and GenAI solutions embedded into mission‑critical platforms, while adhering to enterprise standards for security, compliance, and model governance. This position is responsible for designing, building, and operating AI/ML solutions end‑to‑end, with strong emphasis on MLOps, ML lifecycle management, and production readiness.

Requirements

Bachelor's degree or equivalent in Computer Science, Computer Information Systems, Management Information Systems, Engineering (any), or related
6+ years overall experience in software engineering with strong hands‑on development in Python
3+ years of hands‑on AI/ML experience, building and deploying machine learning models and Gen AI solutions using locally hosted LLMs in production environments
Proven experience productionizing ML models using MLflow and enterprise‑grade MLOps frameworks
Strong understanding of the end‑to‑end ML lifecycle: data preparation, feature engineering, training, validation, deployment, monitoring, and retraining
Experience building RESTful APIs and microservices to expose ML capabilities
Hands‑on experience with CI/CD pipelines, automation, and DevOps practices for ML and application workloads
Experience with containerization and deployment technologies (e.g., Openshift, Docker or equivalent enterprise platforms)
Proficiency with version control and enterprise SDLC tools (Git/Bitbucket, Jenkins, pytest, SonarQube, Artifactory, etc.)
Experience working in large, multi‑team enterprise environments with shared codebases and governance standards
Strong analytical, problem‑solving, and communication skills with ability to engage business and technical stakeholders

Nice To Haves

Experience applying GenAI / LLM‑based solutions (e.g., RAG, summarization, intelligent extraction) to operational and financial services use cases
Exposure to model governance, risk management, and compliance controls in regulated environments
Experience building reusable AI frameworks, utilities, or platforms that can be leveraged across multiple teams
Familiarity with databases, caches, and messaging platforms (e.g., Oracle, MongoDB, Redis, event‑driven architectures)
Experience with cloud or hybrid enterprise AI platforms and observability tools

Responsibilities

Works across development teams to contribute to the story refinement and delivery of data requirements through the delivery life cycle
Leverages architecture components in solution development, codes solutions to integrate, clean, transform, and control data in operational and analytical data systems per acceptance criteria
Builds processes supporting data transformation, data structures, metadata, data quality controls, dependency, and workload management and defines and builds data pipelines and complex data sets to enable data-informed decision making, identifying and raising risks at all stages of the data engineering process
Develops and executes test plans to produce quantitative results, contributes to existing test suites including integration, regression, and performance, analyzes test reports, identifies test issues and errors, and triages underlying causes
Drives complex information technology projects to ensure on-time delivery and adheres to team delivery and release processes
Identifies, defines, and documents data engineering requirements, communicating required information for deployment, maintenance, support, and business functionality
Works with technology partners and a diverse set of stakeholders to identify and close gaps in data management standards adherence, negotiates paths forward, and helps identify and communicate solutions to complex data problems leveraging knowledge of information systems, techniques, and processes.