Data Engineer – Python/AI

Bank of America

Charlotte (NC)

On-site

USD 140,000 - 180,000

Full time

3 days ago

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Job summary

Bank of America is seeking an experienced AI/ML Engineer to design, build, and operate data-driven ML solutions within a large enterprise. You will work with product, operations, and engineering teams to deploy AI capabilities, ensure model governance, security, and compliance, and drive ML lifecycle excellence.

The role emphasizes MLOps, production readiness, and collaboration across multi-team environments in Charlotte, NC.

Qualifications

Bachelor’s degree or equivalent in Computer Science, Information Systems, Engineering, or related fields.
6+ years of software engineering with strong Python development.
3+ years of AI/ML experience with productionizing models.
Experience deploying ML models with MLflow and MLOps
Understanding end-to-end ML lifecycle: data prep, feature engineering, training, validation, deployment, monitoring, retraining.
Experience building RESTful APIs and microservices for ML capabilities.
CI/CD, automation, DevOps practices for ML and apps.
Experience with containerization (OpenShift/Docker) and enterprise platforms.
Proficient in version control and SDLC tools (Git/Bitbucket, Jenkins, pytest, SonarQube, Artifactory).
Experience in large, multi-team enterprise environments and stakeholder communication.

Responsibilities

Collaborate with development teams to refine data requirements and deliverables.
Code data integration, cleaning, transformation, and governance per acceptance criteria.
Build data pipelines, data structures, metadata, data quality controls, and workload management.
Develop and execute tests; contribute to test suites and analyze results.
Lead complex IT projects to ensure on-time delivery and adherence to processes.
Document data engineering requirements for deployment, maintenance, and business use.
Engage with partners and stakeholders to address data management gaps and propose solutions.

Skills

Python

AI/ML development

ML lifecycle

RESTful APIs

CI/CD

DevOps practices

Data engineering

Git/Bitbucket

Jenkins

Docker/OpenShift

Testing/QA

Communication

Education

Bachelor’s degree or equivalent in Computer Science/Engineering

Tools

MLflow

OpenShift

Docker

Jenkins

pytest

SonarQube

Artifactory

Git

Job description

Job Description:

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!

Job Description:

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.

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.

Required Qualifications:

Bachelor's degree or equivalent in Computer Science, Computer Information Systems, Management Information Systems, Engineering (any), or related: and
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

Desired Qualifications:

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

Skills: