Senior Data Engineer: Python, AI/ML & GenAI Pipelines

Careerwebsite; Charlotte, Northern (NC, KY); Hybrid; USD 120,000 - 180,000; Full time

Job summary

Bank of America is seeking a senior AI/ML engineer to design and deliver data solutions that enable technology and business goals. You will integrate, clean, transform, and control data across systems, collaborating with Product and Software Engineering to implement data requirements and troubleshoot data problems.

You will work on AI/ML and GenAI capabilities within global lending platforms, focusing on productionising models, ML lifecycle, and governance, while ensuring security and compliance

Qualifications

• Bachelor's degree or equivalent in Computer Science, Computer Information Systems, Management Information Systems, Engineering (any), or related field.
• 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 ML models and Gen AI solutions.
• Proven experience productionising ML models using MLflow and enterprise-grade MLOps frameworks.
• Strong understanding of the end-to-end ML lifecycle: data prep, feature engineering, training, validation, deployment, monitoring, 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).
• Proficiency with version control and enterprise SDLC tools (Git/Bitbucket, Jenkins, pytest, SonarQube, Artifactory).
• Experience working in large, multi-team enterprise environments with governance standards.
• Strong analytical, problem solving, and communication skills with stakeholders.

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 for data-informed decision making, identifying and raising risks at all stages of data engineering.
• 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 stakeholders to close gaps in data management standards adherence and communicates solutions to complex data problems.

Skills

Analytical Thinking

Application Development

Data Management

DevOps Practices

Solution Design

Agile Practices

Collaboration

Decision Making

Risk Management

Test Engineering

Architecture

Business Acumen

Data Quality Management

Financial Management

Solution Delivery Process

Education

Bachelor's degree or equivalent in Computer Science, Computer Information Systems, MIS, Engineering or related

Tools

OpenShift

Docker

Git

Bitbucket

Jenkins

pytest

SonarQube

Artifactory

Job description

Bank of America is seeking a senior AI/ML engineer to design and deliver data solutions that enable technology and business goals. You will integrate, clean, transform, and control data across systems, collaborating with Product and Software Engineering to implement data requirements and troubleshoot data problems.

You will work on AI/ML and GenAI capabilities within global lending platforms, focusing on productionising models, ML lifecycle, and governance, while ensuring security and compliance