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About This Role
REQUISITION ID: 5374
Location: Remote\- United States Travel Requirement: Some travel may be required
Inovalon was founded in 1998 on the belief that technology, and data specifically, would empower the transformation of the entire healthcare ecosystem for the better, improving both outcomes and economics. At Inovalon, we believe that when our customers are successful in their missions, healthcare improves. Therefore, we focus on empowering them with data\-driven solutions. And the momentum is building.
Together, as ONE Inovalon, we are a united force delivering solutions that address healthcare’s greatest needs. Through our mission\-based culture of inclusion and innovation, our organization brings value not just to our customers, but to the millions of patients and members they serve.
Overview: Inovalon is seeking a Sr. Business Analyst – AI Agent Operator to join its AI Prototyping \& Automation team. This role combines analytical rigor with hands\-on AI execution. The Sr. Business Analyst identifies and quantifies AI automation opportunities across the enterprise and builds the working prototypes that validate them. Rather than handing specifications to a separate build team, this analyst proves the business case by shipping a functional prototype that stakeholders can evaluate and engineering can productionize.
This is a hybrid role for analysts fluent in modern AI tooling — people who can do the financial modeling and stakeholder work of a traditional Sr. BA and also use AI tooling to prototype and demonstrate solutions before committing to engineering investment. Production builds, ongoing maintenance, and operational ownership of deployed systems sit with engineering and AI Operations counterparts.
This is a fast\-moving environment where priorities shift, requirements are often ambiguous, and the tooling evolves weekly. The right person for this role is energized by that — not frustrated by it. They ask questions, chase context on their own, learn new tools before being asked to, and treat uncertainty as a starting condition rather than a blocker.
Duties and Responsibilities:
### Opportunity Identification \& Business Cases
- Maintain a pipeline of AI automation opportunities sourced from business unit intake, executive priorities, and direct stakeholder discovery.
- Build business cases with quantified ROI: cost savings, revenue lift, hours offloaded, quality improvements.
- Translate ambiguous business problems into well\-scoped automation candidates with clear acceptance criteria.
- Track opportunity status, prioritization, and outcomes against original projections.
### Prototype \& Validate
- Build functional prototypes of proposed agents and automations using modern AI tooling (LLM coding assistants, agent frameworks, prompt and skill libraries).
- Validate or invalidate business cases by getting a working version in front of stakeholders within days, not quarters.
- Iterate prototypes against real user feedback before committing to engineering investment.
- Produce demo\-ready artifacts that move executive decisions forward.
- Hand off validated prototypes to engineering with clear specifications, success criteria, and stakeholder context.
### Impact Tracking
- Deliver monthly executive reports covering pipeline health, in\-flight initiatives, and realized impact.
- Conduct post\-implementation reviews comparing projected to actual ROI; capture lessons that improve future estimates.
### Other Responsibilities
- Maintain compliance with Inovalon’s policies, procedures, and mission statement.
- Adhere to all confidentiality and HIPAA requirements in accordance with Inovalon Operating Policies and Procedures.
- Fulfill additional responsibilities reasonably assigned to support the operational and financial success of Inovalon.
Job Requirements:
- 5\+ years in business analysis, management consulting, product operations, or analyst roles.
- Hands\-on experience using LLMs and agent tooling — prompting, evaluations, and workflow orchestration. Candidates should be prepared to walk through a prototype they have built.
- Working proficiency in Python and SQL: sufficient to query databases, parse API responses, manipulate structured data, and debug agent workflows — not expected to write production backend code.
- Strong financial acumen: ROI modeling, sensitivity analysis, cost\-benefit frameworks.
- Strong written and verbal communication; comfortable in front of executives and engineers alike.
- Intrinsic curiosity and a bias toward action; comfortable operating with incomplete information, learning new tools independently, and moving quickly in ambiguous environments.
- Experience with modern agent frameworks (LangGraph, AutoGen, MCP, or equivalent) preferred.
- Domain knowledge in a compliance\-heavy industry such as healthcare, financial services, or insurance preferred.
- Background in product management, business operations, or technology consulting preferred.
- Familiarity with continuous improvement methodologies is a plus.
- Experience in healthcare IT, SaaS, or highly regulated industries is a plus.
Education:
- Bachelor’s or Master’s in Business, Engineering, Computer Science, or related field preferred.
Physical Demands and Work Environment:
- Sedentary work (i.e. sitting for long periods of time);
- Exerting up to 10 pounds of force occasionally and/or negligible amount of force;
- Frequently or constantly to lift, carry push, pull or otherwise move objects and repetitive motions
- Subject to inside environmental conditions; and
- Travel for this position may be minimal with visits to Employer office locations as well as other general related (e.g. conferences, management related) travel.
Inovalon Offers a Competitive Salary and Benefits Package
*In addition to the base compensation, this position may be eligible for performance\-based incentives.*
*The actual base pay offered may vary depending on multiple factors including, but not limited to, job\-related knowledge/skills, experience, business needs, geographical location, and internal equity. At Inovalon, it is not typical for an individual to be hired at or near the top end of the range for their role, and compensation decisions are dependent upon the facts and circumstances of each position and candidate.*
*Inovalon invests in associates to help them stay healthy, save for long\-term financial goals, and manage the demands of work and personal commitments. That’s why Inovalon offers a valuable* *benefits package* *with a wide range of choices to meet associate needs, which may include health insurance, life insurance, company\-paid disability, 401k, 18\+ days of paid time off, and more.*
Base Compensation Range
$95,000—$144,000 USD*This position is not eligible for immigration sponsorship (e.g. H\-1B, TN, or E\-3\). Applicants must be authorized to work in the United States as a condition of employment. (This is only applicable for US\-based positions)*
*If you don’t meet every qualification listed but are excited about our mission and the work described, we encourage you to apply. Inovalon is most interested in finding the best candidate for the job,* *and you may be just the right person for this or other roles.*
*By embracing* *inclusion,* *we enhance our work environment and drive business success. Inovalon strives to* *provide equal opportunities* *to* *the communities where we operate and to our clients and everyone whom we serve. We endeavor to create a culture of inclusion in which our associates feel empowered to bring their full, authentic selves to work and pursue their professional goals in an equitable setting. We understand that by fostering this type of culture, and welcoming different perspectives, we generate innovation and growth.*
*Inovalon is proud to be an equal opportunity workplace. We are committed to equal employment opportunity regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity or* *veteran status. We also consider qualified applicants regardless of criminal histories, consistent with legal requirement.*
Salary Context
This $95K-$144K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $180K across 2130 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 4,133 AI roles we're tracking, AI/ML Engineer positions make up 69% of the market. At Inovalon, 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 $185,000 based on 13,200 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($119K) sits 35% below the category median. Disclosed range: $95K to $144K.
Across all AI roles, the market median is $200,700. Top-quartile compensation starts at $254,000. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Safety ($274,200) and AI Engineering Manager ($268,700). By seniority level: Entry: $97,760; Mid: $165,778; Senior: $227,400; Director: $250,000; VP: $250,000.
Inovalon AI Hiring
Inovalon has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Remote, US. Compensation range: $144K - $144K.
Remote Work Context
Remote AI roles pay a median of $173,300 across 2,012 positions. About 14% of all AI roles offer remote work.
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 4,133 open positions tracked in our dataset. By seniority: 106 entry-level, 1,901 mid-level, 1,663 senior, and 463 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (583 positions). The remaining 3,532 roles require on-site or hybrid attendance.
The market median for AI roles is $200,700. Top-quartile compensation starts at $254,000. The 90th percentile reaches $307,500. Highest-paying categories: AI Safety ($274,200 median, 57 roles); AI Engineering Manager ($268,700 median, 42 roles); Research Engineer ($260,000 median, 442 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 4,133 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,865), Data Scientist (339), AI Software Engineer (313). 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 (106) are outnumbered by mid-level (1,901) and senior (1,663) 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 463 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (583 positions), with 3,532 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 $200,700. Top-quartile roles start at $254,000, and the 90th percentile reaches $307,500. 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 $274,200 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 (2,128 postings), Aws (1,324 postings), Azure (1,003 postings), Rag (916 postings), Gcp (817 postings), Pytorch (655 postings), Prompt Engineering (639 postings), Claude (571 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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