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About This Role
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!
Position Summary
The Artificial Intelligence (AI) Sr. Data Engineer will design, build, validate, and operationalize AI\-enabled solutions that improve infrastructure, technology operations, and enterprise decision\-making across hybrid cloud and on\-premises environments. The role partners with infrastructure engineering, architecture, operations, cyber/risk, model governance, data science, and product teams to convert business and technology needs into secure, scalable, measurable capabilities.
The ideal candidate combines applied data science, natural language processing, machine learning, automation, model validation, and software engineering experience with the discipline to deliver production\-ready solutions in a regulated enterprise environment. This role requires strong technical execution, governance awareness, stakeholder communication, and the ability to move AI/ML capabilities from concept through deployment, monitoring, and continuous improvement.
Key Responsibilities
- Design, develop, test, validate, and deploy AI/ML\-enabled capabilities that improve infrastructure reliability, capacity forecasting, observability, operational automation, and enterprise decision\-making
- Apply natural language processing, statistical modeling, supervised learning, unsupervised learning, embeddings, classification, anomaly detection, forecasting, and optimization techniques to complex enterprise data sets
- Build reusable models, data pipelines, APIs, feature workflows, prompt libraries, automation components, dashboards, and integration patterns across technology, risk, operations, and platform domains
- Support the full model lifecycle, including use case intake, data preparation, model training, model selection, validation readiness, deployment, monitoring, ongoing performance review, and remediation planning
- Provide analytical and technical challenge to AI/ML solutions by assessing model design, assumptions, limitations, performance, controls, explainability, and implementation risks
- Partner with infrastructure, data science, model risk, cyber/risk, architecture, operations, and product teams to define requirements, success metrics, delivery plans, governance artifacts, and operational handoff criteria
- Develop production\-grade code, reusable documentation, model artifacts, validation evidence, test automation, and implementation procedures aligned to enterprise engineering and governance standards
- Advance MLOps, CI/CD, version control, model serving, workflow orchestration, monitoring, and hybrid cloud deployment practices for AI\-enabled infrastructure services
- Communicate technical findings, model outcomes, operational impact, implementation risks, and tradeoffs clearly to engineering teams, senior stakeholders, governance partners, and cross\-functional leaders
Required Qualifications
- 15\+ years of experience delivering data science, software engineering, analytics, automation, platform engineering, risk analytics, cloud engineering, SRE, or infrastructure technology solutions
- 7\+ years of hands\-on experience applying AI/ML, NLP, statistical modeling, predictive analytics, optimization, or quantitative methods to enterprise business, risk, technology, or operational problems
- Strong Python programming skills and practical experience with data science, machine learning, or NLP libraries such as pandas, NumPy, scikit\-learn, TensorFlow, PyTorch, spaCy, Hugging Face Transformers, Gensim, or equivalent tools
- Experience with the end\-to\-end model lifecycle, including model ideation, data preparation, training, selection, validation, deployment, ongoing monitoring, performance review, and governance documentation
- Experience developing NLP, text analytics, classification, embeddings, recommendation, key driver analysis, network analysis, anomaly detection, or predictive modeling solutions
- Experience creating model documentation, validation evidence, implementation procedures, monitoring plans, governance artifacts, or peer review materials in a large enterprise environment
- Working knowledge of APIs, data pipelines, relational databases, SQL, dashboards, visualization tools, automation frameworks, version control, CI/CD, observability, and production support practices
- Ability to analyze complex structured and unstructured data, identify patterns, convert insights into engineering action, and quantify business or operational impact through metrics and reporting
- Demonstrated experience working in Agile delivery environments using tools such as Jira, Kanban boards, Confluence, and related delivery or documentation platforms
- Excellent written and verbal communication skills, with the ability to explain model behavior, technical findings, operational risks, governance requirements, and implementation tradeoffs to technical and executive audiences
- Highly motivated, self\-directed, and comfortable operating across multiple initiatives in a large, matrixed, geographically distributed technology organization
Desired Qualifications:
- BA or BS in Computer Science, Data Science, Engineering, Mathematics, Statistics, Information Systems, Artificial Intelligence, Business Analytics, Business Administration, or a related quantitative or technical field; advanced Masters degree preferred
- Experience developing AI/ML solutions for infrastructure operations, capacity forecasting, incident prediction, anomaly detection, root\-cause analysis, configuration intelligence, automated remediation, or operational excellence
- Experience with generative AI, large language models, prompt engineering, reusable prompt libraries, AI\-assisted workflows, model validation guidance, or GenAI governance practices
- Experience leading or managing data science, NLP, model governance, or AI enablement initiatives across multiple stakeholders or teams
- Experience with enterprise AI infrastructure platforms, model\-serving frameworks, GPU or accelerated compute environments, Red Hat OpenShift AI, NVIDIA AI platforms, or comparable AI/ML infrastructure technologies
- Experience integrating AI solutions with enterprise monitoring, observability, workflow orchestration, API, dashboarding, or automation platforms such as Tableau, Streamlit, Shiny, Jupyter, or equivalent tools
- Experience working in regulated environments with model risk management, validation, peer review, data governance, privacy, security, audit, and compliance requirements
- Ability to influence technical direction, establish reusable processes, develop best practices, and communicate effectively with geographically dispersed engineering, operations, architecture, risk, and business partners
Skills:
- Analytical Thinking
- Application Development
- Data Management
- Risk Management
- Solution Design
- Agile Practices
- Architecture
- Collaboration
- Decision Making
- DevOps Practices
- Business Acumen
- Data Quality Management
- Financial Management
- Solution Delivery Process
- Test Engineering
Shift:
1st shift (United States of America)Hours Per Week:
40
Salary Context
This $128K-$182K range is above the median for Data Engineer roles in our dataset (median: $153K across 35 roles with salary data).
Role Details
About This Role
Data Engineers build the pipelines that feed AI models. They design ETL workflows, manage data lakes, and ensure training and inference data is clean, timely, and accessible. Without good data engineering, AI projects fail. It's that simple.
The AI era has expanded the data engineer's scope far beyond batch ETL jobs. You're building real-time embedding pipelines for RAG systems, managing vector databases, ensuring training data quality at scale, and building the infrastructure that lets ML teams iterate on data as fast as they iterate on models. Data quality is the biggest predictor of model quality, and you're the person responsible for it.
Across the 4,317 AI roles we're tracking, Data Engineer positions make up 1% of the market. At Bank of America, this role fits into their broader AI and engineering organization.
Data Engineer demand in AI contexts is strong and growing. Every company building AI needs clean, reliable data pipelines. The shift toward real-time AI applications (chatbots, recommendation engines, agent systems) means data engineering is more critical than ever. Companies are willing to pay premium salaries for data engineers with AI/ML pipeline experience.
What the Work Looks Like
A typical week includes: debugging a data pipeline that's producing stale embeddings for the RAG system, optimizing a Spark job that processes training data, building a data quality monitoring dashboard, meeting with the ML team to understand their next data requirements, and writing dbt models that transform raw event data into ML-ready features. The work is deeply technical and high-impact.
Data Engineer demand in AI contexts is strong and growing. Every company building AI needs clean, reliable data pipelines. The shift toward real-time AI applications (chatbots, recommendation engines, agent systems) means data engineering is more critical than ever. Companies are willing to pay premium salaries for data engineers with AI/ML pipeline experience.
Skills Required
SQL, Python, and distributed systems (Spark, Airflow, dbt) are core. Cloud data platforms (Snowflake, BigQuery, Redshift) are increasingly standard. Many AI-focused roles also want familiarity with vector databases and embedding pipelines. Understanding data modeling, pipeline orchestration, and data quality frameworks covers the essentials.
AI-specific data engineering skills include: building feature stores, managing training data versioning, implementing data lineage tracking, and building real-time embedding pipelines. Experience with streaming systems (Kafka, Flink) is valuable for real-time AI applications. Understanding ML data requirements (balanced datasets, data augmentation, evaluation set construction) makes you much more effective working with ML teams.
Strong postings specify the data stack, mention ML pipeline work, and describe the scale of data you'll be working with. Look for companies that understand the connection between data quality and model quality. Avoid roles that conflate data engineering with data analysis.
Compensation Benchmarks
Data Engineer roles pay a median of $185,000 based on 83 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($155K) sits 16% below the category median. Disclosed range: $128K to $182K.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
Bank of America AI Hiring
Bank of America has 6 open AI roles right now. They're hiring across Data Engineer, AI Software Engineer, AI/ML Engineer. Positions span New York, NY, US, Addison, TX, US, Charlotte, NC, US. Compensation range: $182K - $230K.
Location Context
AI roles in New York pay a median of $220,000 across 1,650 tracked positions.
Career Path
Common paths into Data Engineer roles include Backend Engineer, Database Administrator, Analytics Engineer.
From here, career progression typically leads toward Senior Data Engineer, ML Engineer, Data Platform Lead.
Master SQL and Python first. Then learn a distributed processing framework (Spark or its modern alternatives) and a pipeline orchestrator (Airflow, Dagster, Prefect). Build a portfolio project that demonstrates end-to-end pipeline construction: ingest, transform, validate, serve. If you want to specialize in AI data engineering, add vector databases and embedding pipelines to your skill set.
What to Expect in Interviews
Expect SQL deep-dives (query optimization, partitioning strategies, data modeling), Python coding focused on data pipeline patterns, and system design questions about building scalable ETL workflows. Companies with ML teams will ask about feature stores, embedding pipelines, and training data management. Be ready to discuss data quality monitoring, pipeline orchestration, and how you'd handle schema evolution in a production data lake.
When evaluating opportunities: Strong postings specify the data stack, mention ML pipeline work, and describe the scale of data you'll be working with. Look for companies that understand the connection between data quality and model quality. Avoid roles that conflate data engineering with data analysis.
AI Hiring Overview
The AI job market has 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 roles).
Data Engineer demand in AI contexts is strong and growing. Every company building AI needs clean, reliable data pipelines. The shift toward real-time AI applications (chatbots, recommendation engines, agent systems) means data engineering is more critical than ever. Companies are willing to pay premium salaries for data engineers with AI/ML pipeline experience.
The AI Job Market Today
The AI job market spans 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (138) are outnumbered by mid-level (2,071) and senior (1,655) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $287,500 median, while Prompt Engineer roles sit at $145,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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