FREE ACCESS
5,000–10,000 jobs/day
See all jobs on Scoutfield
Search thousands of fresh jobs every day.
Discover
- Fresh listings
- Fast filters
- No subscription required
Create a free account and start exploring right away.
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, Product, Operations, and Software Engineering teams to implement data requirements
- Design, build, deploy, scale, and operate end-to-end AI/ML and GenAI solutions
- Productionize AI-driven capabilities for lending and payments processes
- Apply MLOps, ML lifecycle management, model governance, security, and compliance standards
- Contribute to story refinement and delivery of data requirements throughout the delivery lifecycle
- Build data transformation processes, data structures, metadata, data quality controls, dependencies, workloads, data pipelines, and complex data sets
- Develop and execute integration, regression, and performance test plans; analyze reports and triage issues
- Drive complex technology projects toward on-time delivery while following release processes
- Define and document data engineering requirements and deployment, maintenance, support, and business functionality information
- Identify and resolve gaps in data management standards and troubleshoot complex data problems
Requirements
What you’ll need- 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 GenAI 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 such as Openshift, Docker, or equivalent enterprise platforms
- 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 across multiple teams
- 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 teammates’ 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