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About This Role
Overview: Summary/Objective
The Business Analyst (BA), Internal Systems and AI Automation owns business analysis, requirements, testing, delivery support, and product-governance activities for internally developed systems, AI, and process automation initiatives. The role leads discovery with business teams, translates opportunities into development-ready requirements, and supports initiatives through delivery, adoption, and measurable business value.
This role partners with other Business Analysts to maintain a combined Innovation roadmap, coordinate dependencies, and prioritize work across the Internal Systems and AI portfolios. Although the role will have a strong focus on identifying and advancing AI and automation opportunities, responsibilities may shift across the combined portfolio based on business priorities.
Reporting to the VP of Strategy, this role works closely with Enterprise Intelligence and Systems Development teams, and cross-functional business leaders to move initiatives from concept through delivery, adoption, and measurable business value.
Responsibilities: Essential Functions
Reasonable accommodation may be made to enable individuals with disabilities to perform the essential functions.
Business Analysis, Requirements, & Testing
- Lead discovery with functional teams through interviews, shadowing, and workflow observation to identify problems worth solving.
- Analyze current-state workflows and recommend future-state processes that may involve process changes, internal systems, AI, automation, or a combination of solutions.
- Translate user needs into clear requirements, user stories, acceptance criteria, and mockups ready for development handoff.
- Own or coordinate testing for assigned initiatives, including user acceptance testing, prototype validation, and structured post-implementation feedback.
- Coordinate with other Business Analysts on initiatives that cross Internal Systems and AI portfolios, including shared workflows, integrations, data, and internal or client-facing user experiences.
- Maintain traceability from the original business need through governance review, development, testing, delivery, and adoption.
Roadmap, Prioritization, & Product Governance
- Provide product-governance ownership for assigned internal systems, AI, and automation initiatives, including intake, evaluation, prioritization, roadmap planning, stage-gate readiness, and outcome tracking.
- Partner with other Business Analysts to maintain a combined Innovation roadmap, coordinate dependencies, and sequence priorities against Enterprise Intelligence and Systems Development capacity.
- Define product vision, success measures, business cases, and release recommendations for AI and automation initiatives in partnership with Innovation, development leadership, and business stakeholders.
- Facilitate backlog prioritization and document trade-off recommendations based on strategic value, business impact, feasibility, risk, dependencies, and available capacity.
- Present roadmap status, dependencies, risks, and recommendations to executive stakeholders and governance committees, including the Systems Governance Committee.
- Help establish and continuously improve product-governance, roadmap-management, and delivery-tracking practices, frameworks, and documentation standards across the Innovation team.
AI Opportunity & Innovation Leadership
- Identify, evaluate, and champion opportunities where process improvement, internal systems, AI, or automation can improve efficiency, quality, employee experience, or business outcomes.
- Apply established readiness, viability, risk, and value frameworks to evaluate proposed AI and automation opportunities
- Partner with data science and engineering teams to assess feasibility, scope pilots, and move validated AI concepts into production.
- Stay current on the AI tooling landscape (LLMs, agents, automation platforms) and translate developments into practical opportunities for the business.
- Continuously evaluate internally developed AI and systems against current market technology to identify modernization opportunities.
- Introduce identified opportunities into the established Innovation intake and roadmap process for evaluation, prioritization, and governance review
Client-Facing Support (Limited Scope)
- Prepare client-facing materials (feature summaries, demo scripts, talking points) on internally developed AI, user-interface, and internal-systems capabilities for Account Management and Strategic Advisor use. This role does not serve as the organization's subject-matter authority on AI for clients, prospects, auditors, or examiners.
- Support Account Management and Strategic Advisors on an ad hoc basis, including occasional participation in client calls to gather feedback, clarify business needs, or identify requirements related to internally developed AI, user-interface, and reporting capabilities.
Initiative Tracking & Adoption
- Maintain transparent tracking of assigned Internal Systems, AI, and automation initiatives from intake through delivery and adoption, keeping status, dependencies, blockers, decisions, and expected outcomes visible to stakeholders.
- Define and report adoption metrics for internal AI initiatives — usage, engagement, accuracy/quality, and business value realized.
- Drive adoption through release communication, training support, and feedback loops; identify and address barriers when adoption lags.
- Prepare reporting and business cases that frame the strategic and financial rationale for new and continuing initiatives.
- Maintain requirements, testing evidence, approvals, decisions, and related documentation sufficient to support internal audit and governance requirements.
Competencies
- Ownership & Execution – Takes ownership of problems from discovery through adoption, drives work and recommendations forward, and follows through on agreed outcomes with limited direction.
- Strategic & Business Acumen – Connects analysis and delivery work to business value; builds credible cases for investment and prioritization.
- AI Fluency – Maintains practical, current knowledge of AI capabilities, limitations, risks, and governance expectations.
- Communication & Influence – Communicates clearly with technical and non-technical audiences, including executives and internal stakeholders.
- Adaptability & Learning Agility – Learns new tools, domains, and methods quickly as the business and technology landscape evolve.
Position Details & Working Conditions
Supervisory Responsibility: This position has no direct reports.
Position Type and Expected Hours of Work: Full-time, salary position. Hours of work and days are variable, but typically Monday through Friday, 7 AM – 4 PM.
Travel: Limited travel; travel to satellite offices, client locations, industry events, or offsite meeting locations is expected for this position.Work Environment: This job operates in a professional office environment. This role routinely uses standard office equipment such as computers, phones, scanners, and copy machines.Physical Demands: The physical demands described here are representative of those that must be met by an employee to successfully perform the essential functions of this job. While performing the duties of this job, the employee is regularly required to talk or hear. The employee is frequently required to sit for long periods of time; use hands to type, handle or feel; and reach with hands and arms. The employee is occasionally required to stoop or kneel. The employee must occasionally lift and move up to 25 pounds. Specific vision abilities required by this job include close vision, distance vision, color vision, peripheral vision, depth perception, and the ability to adjust focus. Qualifications: Education and Experience
Required:* Three or more years of progressive experience in business analysis, product governance, product management, or a related discipline, with demonstrated responsibility for moving products or initiatives from discovery through delivery and adoption.
- Demonstrated experience evaluating or applying AI, workflow automation, or other technology solutions to business processes, including assessing feasibility, output quality, implementation considerations, and business value.
- Experience supporting internally developed systems or technology-enabled process improvement initiatives through requirements, testing, delivery, and adoption.
- Proven ability to translate user needs into development-ready requirements, user stories, and acceptance criteria.
- Experience building and presenting roadmaps, business cases, and executive-level reporting.
- Strong analytical and problem-solving skills; experience defining and tracking product or adoption metrics.
- Proficiency with Jira (or similar), Excel, and analytics/reporting tools such as Power BI.
- Demonstrated experience applying SDLC governance and gating processes.
- Basic SQL or database querying skills to support product analytics and data-driven decision making.
Preferred:* Experience in regulated industries (banking, financial services, or MSP environments) and familiarity with regulatory expectations for technology and AI (e.g., FFIEC, GLBA).
- Experience with AI governance, responsible-use frameworks, or AI risk assessment.
- Ability to build wireframes or low-fidelity prototypes to support product ideation.
- One or more relevant product or project management certifications (e.g., CSPO, POPM, PMP, PMI-ACP, PMI-PBA, or AI-focused certifications).
- Bachelor's or master's degree in business, information systems, or a related field, or equivalent practical experience.
Foundational understanding of IT environments (operating systems, networking, hardware/software) to support conversations with technical teams. *
Other Duties
Please note this job description is not designed to cover or contain a comprehensive listing of activities, duties, or responsibilities that are required of the employee for this job. Duties, responsibilities, and activities may change at any time with or without notice.
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 8,943 AI roles we're tracking, AI/ML Engineer positions make up 75% of the market. At Navanta, 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 $212,000 based on 11,866 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200.
Across all AI roles, the market median is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,250; VP: $235,000.
Navanta AI Hiring
Navanta has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Oklahoma City, OK, US.
Location Context
Across all AI roles, 6% (521 positions) offer remote work, while 8,351 require on-site attendance. Top AI hiring metros: New York (2,878 roles, $221,000 median); San Francisco (2,416 roles, $263,811 median); Los Angeles (1,340 roles, $210,000 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 8,943 open positions tracked in our dataset. By seniority: 175 entry-level, 4,132 mid-level, 3,268 senior, and 1,368 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (521 positions). The remaining 8,351 roles require on-site or hybrid attendance.
The market median for AI roles is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 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 8,943 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,739), AI Product Manager (403), AI Software Engineer (402). 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 (175) are outnumbered by mid-level (4,132) and senior (3,268) 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 1,368 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (521 positions), with 8,351 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 $213,380. Top-quartile roles start at $264,148, and the 90th percentile reaches $319,310. 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 $253,100 median, while Prompt Engineer roles sit at $141,440. 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 (1,422 postings), Aws (808 postings), Azure (638 postings), Rag (555 postings), Gcp (457 postings), Pytorch (433 postings), Claude (341 postings), Kubernetes (337 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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