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About This Role
Position Summary
The client's Division of Consumer and Community Affairs (DCCA) is establishing an AI Lab to explore and implement generative AI and machine learning solutions that enhance staff productivity, improve analytical capabilities, and strengthen the Division's work in consumer protection and community development. The AI Lab is developing a portfolio of AI/ML applications, tools, and solutions that require rigorous requirements management,
stakeholder coordination, compliance documentation, and agile project oversight.
We are looking for a Senior Business Analyst to support the AI Lab Director by anchoring the lab's requirements gathering, project coordination, governance activities, and stakeholder
engagement. This role serves as the bridge between technical data scientists and business
stakeholders, translating business needs into technical requirements, managing agile workflows, ensuring compliance with client IT governance frameworks, and coordinating testing and
deployment activities. The Senior BA will serve as a key advisor and operational partner to the AI Lab Director, providing strategic insights on project portfolio management, compliance
readiness, and stakeholder needs, while ensuring that AI/ML solutions align with business objectives and meet federal security, privacy, and compliance obligations.
The AI Lab operates as a small, agile team where roles are fluid and practitioners collaborate closely across technical and business functions. This position requires someone who can work independently to manage complex AI/ML projects, direct compliance documentation efforts, facilitate agile ceremonies, and communicate effectively with diverse audiences ranging from
data scientists and engineers to economists, attorneys, and senior leadership. The Senior BA will support the AI Lab Director in strategic planning, portfolio prioritization, and executive
communications.
Responsibilities
Requirements Gathering and Business Analysis
Evaluate and analyze business requirements from DCCA staff, leadership, and end\-user needs for AI/ML solutions; recommend and design solutions that meet business needs
Translate business requirements into structured functional specifications, user stories, and acceptance criteria that data scientists and engineers can build against
Work directly with economists, bank examiners, policy analysts, and attorneys to elicit, refine, and document requirements for AI\-enabled tools and applications
Evaluate and advise on options, risks, costs versus benefits, system impacts, and business and technology priorities for AI/ML initiatives
Conduct gap analysis and business process improvement assessments to identify opportunities for AI/ML automation and efficiency gains
Develop process flow diagrams, use cases, mockups, and workflow documentation to support AI/ML application design
Agile Project Management and Coordination
Direct and facilitate agile processes for AI Lab projects using Scrum and/or Kanban methodologies, including sprint planning, daily standups, retrospectives, and backlog grooming
Coordinate development timelines, deliverables, and dependencies across AI/ML projects, ensuring alignment with stakeholder expectations and divisional priorities
Manage project roadmaps, action plans, and strategic plans for AI Lab initiatives; track progress and communicate status to technical teams and business stakeholders
Break down complex AI/ML initiatives into manageable epics, features, and user stories; prioritize work based on business value and technical feasibility
Facilitate collaboration between data scientists, AI cloud engineers, business stakeholders, and subject matter experts
Direct and mentor team members on agile best practices and project delivery methodologies
Utilize project management and tracking tools (Jira, Azure DevOps, or similar) to maintain visibility into project status and blockers
AI Governance, Compliance, and Documentation
Direct compliance activities for AI Lab systems, including FISMA documentation, Privacy Impact Assessments (PIAs), Authority to Operate (ATO) processes, and system security plans
Ensure AI/ML systems comply with federal IT governance frameworks including FISMA, FedRAMP, NISPOM, BISP procedures, OMB guidance, and Board privacy requirements
Coordinate security assessments, privacy reviews, and compliance audits for AI/ML applications and systems
Serve as primary point of contact with the client's security, privacy, and compliance offices on matters related to AI Lab systems
Maintain AI Lab system inventory and ensure records are current and aligned with agency reporting requirements
Support data governance activities including data classification, records management, and retention schedule compliance for AI/ML datasets
Participate in information gathering and tracking efforts to ensure ongoing compliance; interpret and apply client requirements to AI/ML contexts
Develop and maintain governance policies and standards for responsible AI development and deployment
Documentation and Communication
Create comprehensive documentation including user guides, process documentation, technical specifications, workflow diagrams, test plans, agile materials, and application manuals
Develop executive summaries, briefing materials, presentations, and talking points for senior leadership on AI Lab initiatives
Prepare documentation packages for security assessments, compliance reviews, and governance audits
Translate complex technical AI/ML concepts into clear communications for non\-technical audiences
Partner and liaise with customers, staff, project team members, sponsors, and stakeholders to manage expectations and ensure customer satisfaction
Present project status, risks, and recommendations to technical and non\-technical audiences including senior leadership
Testing, Quality Assurance, and Deployment Support
Develop and approve functional and system\-level test cases for AI/ML applications; generate, maintain, and track test results
Direct and participate in user acceptance testing (UAT) for AI Lab applications, coordinating with business users to validate functionality
Work in partnership with data scientists and engineers to ensure test coverage, solution deployment readiness, and security vulnerability management
Conduct and oversee performance, integration, baseline, and regression testing activities
Coordinate deployment activities and ensure smooth transitions from development to production environments
Track bugs, issues, and enhancement requests through resolution
Validate that deployed AI/ML solutions meet business requirements and user needs
Strategic Planning and Continuous Improvement
Provide strategic direction for AI Lab service delivery, process improvements, and operational effectiveness
Develop and report on metrics and trends regarding AI Lab services, processes, project delivery, and customer satisfaction
Investigate and escalate complex business\-oriented inefficiencies, workflow issues, and opportunities for improvement
Identify emerging AI technologies and methodologies that could benefit DCCA's mission
Direct business process improvement initiatives that leverage AI/ML capabilities
Required Qualifications
Bachelor's degree in Computer Science, Information Technology, Business Administration, or related field
At least ten years of experience in business analysis, project management, or related roles within technology organizations
Expert knowledge of Agile methodologies (Scrum, Kanban) including demonstrated ability to facilitate agile ceremonies and manage backlogs
Expertise in requirements gathering and business analysis including ability to translate complex business needs into technical specifications
Strong experience with federal IT governance and compliance frameworks including FISMA, FedRAMP, privacy requirements (PIAs), and ATO processes
Project management expertise including demonstrated ability to manage complex technical projects, coordinate cross\-functional teams, and deliver results
Experience with stakeholder management, consulting, and facilitating communication between technical and non\-technical audiences
Knowledge of software development lifecycle (SDLC), release management, and quality assurance practices
Experience creating comprehensive documentation including technical specifications, user guides, process flows, and compliance materials
Strong analytical and problem\-solving skills with ability to evaluate complex situations and recommend solutions
Ability to work independently and direct others in solutions delivery, compliance efforts, and project coordination
Excellent written and verbal communication skills
Preferred Qualifications
Prior experience in U.S. federal government, particularly in regulatory, supervisory, or policy environments
Experience with AI/ML project management or supporting data science teams including understanding of AI/ML development lifecycles and unique governance considerations
Experience supporting or coordinating testing for data science applications or analytics tools
Familiarity with AI governance frameworks and responsible AI practices (bias testing, model documentation, fairness assessments)
Experience with project management tools such as Jira, Azure DevOps, Confluence, or similar platforms
Knowledge of cloud platforms (AWS preferred) and understanding of cloud deployment processes
Experience with process modeling and documentation tools (Visio, Lucidchart, Miro, or similar)
Understanding of data governance, data privacy regulations, and data management best practices
Experience in financial services, banking supervision, consumer finance, or regulatory data environments
Familiarity with test automation concepts and tools
Experience preparing materials for senior leadership and executives
PMP, PMI\-ACP, CBAP, or similar certifications
Coursework or certification in information security, privacy, or compliance (CIPP, CISSP, or equivalent)
VIVA is an equal opportunity employer. All qualified applicants have an equal opportunity for placement, and all employees have an equal opportunity to develop on the job. This means that VIVA will not discriminate against any employee or qualified applicant on the basis of race, color, religion, sex, sexual orientation, gender identity, national origin, disability or protected veteran status
Salary Context
This $35K-$45K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $180K across 1841 roles with salary data).
View full AI/ML Engineer salary data →Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 3,708 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At VIVA, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $218,750 based on 3,817 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $200,000. This role's midpoint ($40K) sits 81% below the category median. Disclosed range: $35K to $45K.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
VIVA AI Hiring
VIVA has 2 open AI roles right now. They're hiring across AI/ML Engineer, Data Scientist. Based in Washington, DC, US. Compensation range: $45K - $45K.
Location Context
Across all AI roles, 14% (508 positions) offer remote work, while 3,180 require on-site attendance. Top AI hiring metros: New York (1,045 roles, $220,000 median); San Francisco (810 roles, $277,088 median); Los Angeles (397 roles, $215,000 median).
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). 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 (102) are outnumbered by mid-level (1,705) and senior (1,469) 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 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 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 $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. 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 $300,000 median, while Prompt Engineer roles sit at $140,000. 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 (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 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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