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Data Engineer – Python, AI
Bank of America. Develop and deliver data solutions supporting technology and business goals .
Tech Stack
Tools & technologiesCloudDockerJenkinsMicroservicesMongoDBOpenShiftOraclePythonRedisSDLC
About the role
Key responsibilities & impact- Develop and deliver data solutions supporting technology and business goals
- Design and deliver code for integrating, cleaning, transforming, and controlling data in operational and analytical systems
- Work with stakeholders and Product and Software Engineering teams to implement data requirements
- Analyze performance and research and troubleshoot data problems
- Contribute to story refinement and delivery of data requirements across the delivery lifecycle
- Build data transformation processes, data structures, metadata, data quality controls, dependencies, and workload management
- Define and build data pipelines and complex datasets for data-informed decision-making
- Develop and execute integration, regression, and performance test plans; analyze reports and triage issues
- Drive complex IT projects through on-time delivery and follow delivery and release processes
- Identify, define, and document data engineering requirements and deployment, maintenance, support, and business functionality information
- Build, deploy, and scale ML and GenAI solutions embedded in enterprise lending and payments platforms
- Design, build, and operate AI/ML solutions end-to-end with emphasis on MLOps, ML lifecycle management, and production readiness
- Collaborate with product, operations, engineering, and diverse stakeholders on data management standards, governance, and complex data solutions
Requirements
What you’ll need- Bachelor's degree or equivalent work experience in Computer Science, Computer Information Systems, Management Information Systems, Engineering, or related field
- 6+ years overall experience in software engineering
- Strong hands-on development in Python
- 3+ years of hands-on AI/ML experience building and deploying machine learning models and GenAI solutions using locally hosted LLMs in production environments
- 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
- Experience with containerization and deployment technologies such as Openshift and Docker
- Proficiency with version control and enterprise SDLC tools including Git/Bitbucket, Jenkins, pytest, SonarQube, and Artifactory
- 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
- Experience applying GenAI/LLM-based solutions such as RAG, summarization, and 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
- Familiarity with databases, caches, and messaging platforms such as Oracle, MongoDB, Redis, and event-driven architectures
- Experience with cloud or hybrid enterprise AI platforms and observability tools
Benefits
Comp & perks- Affordable, competitive, and flexible benefits
- Support for physical, emotional, and financial wellness
- Opportunities to learn, grow, and make an impact
- In-office culture supporting collaboration, engagement, and career development
- 40 hours per week, 1st shift