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About This Role
About Stellix
Stellix helps organizations in the life sciences and other critical industries connect strategy, systems, and teams so they can turn digital and AI investments into measurable operational results. Join our team as we design, implement, and govern the adaptive operating models that produce outcomes that matter to our clients and the communities they serve.
Role Summary
We are seeking an Senior Business Analyst, AI & Workforce Productivity, focusing on Workforce Productivity to help Stellix identify, design, and deliver AI-enabled and automation-driven solutions to create rapid value for the enterprise and our customers.
This is a hands-on, cross-functional individual contributor role that blends business problem solving, solution architecture, workflow automation, and enterprise AI enablement. The successful candidate will partner directly with business leaders and teams to uncover opportunities, map user journeys, define user stories, and rapidly translate needs into practical solutions that improve workforce productivity, accelerate operations, and support service delivery revenue.
This role is centered on outcomes. If you love technology but understand it’s useless unless people, processes and outcomes are moving the business needle, then please apply. If you love technology and implementing it only – do not apply. The right candidate knows how to use AI, automation, and enterprise productivity platforms to solve meaningful business problems, improve how work gets done, and deliver value quickly and repeatedly across the organization. It also requires strong business judgment, and the ability to work independently across functions with minimal direction.
Key Responsibilities
Business Partnership & Opportunity Identification
- Partner with business leaders, functional stakeholders, and operational teams to identify high-value opportunities for AI and automation
- Translate business needs into practical solution concepts that improve productivity, cycle time, quality, operational performance, and service delivery outcomes.
- Independently navigate the organization to build relationships and move initiatives from idea to execution.
- Communicate solution value clearly to both technical and non-technical stakeholders.
- Establish measures of success for each solution, such as time savings, throughput improvement, quality gains, adoption, revenue contribution, or customer-facing impact.
Solution Design & Delivery
- Design and deliver AI-enabled and automation-driven solutions using enterprise tools such as Glean, Microsoft AI, Claude Code, Power Automate, and related platforms.
- Build rapid prototypes, pilots, and production-ready solutions that demonstrate value quickly while supporting long-term scalability.
- Evaluate and apply the right architecture for each use case, including search-based experiences, workflow-triggered AI, copilots, knowledge assistants, and structured automation.
AI Governance
- Establish governance processes, standards, and guardrails for AI and automation solutions to ensure they are deployed responsibly, adopted effectively, and aligned to enterprise priorities.
- Partner with Training team to drive change management activities that help the business adapt how work gets done and adoption planning for new AI-enabled ways of working.
Qualifications
Required Experience
- 8+ years of experience in solution design, business systems, enterprise productivity, workflow automation, digital transformation, or related fields.
- Proven experience working directly with business stakeholders to define needs, shape solutions, and deliver measurable outcomes.
- Demonstrated success translating ambiguous business problems into clear requirements, user stories, workflow designs, and implemented solutions.
- Experience operating as a highly independent, influential individual contributor with strong ownership.
- Experience navigating complex organizations and building alignment without relying on formal authority.
Required Technical Skills
- Experience with enterprise productivity and workflow automation platforms (e.g. Power Automate and similar tools).
- Understanding of AI architecture concepts including LLMs, retrieval-augmented generation, semantic search, prompt design and orchestration patterns.
- Experience using enterprise AI tools and modern AI solution platforms such as Glean, Microsoft AI, Claude Code, or equivalent technologies.
- Ability to design practical end-to-end solutions that combine AI, automation (RPA) and context-bound enterprise knowledge.
- Capability in business process analysis, user journey mapping, and user story definition.
- Ability to evaluate solution feasibility across business value, technical complexity, adoption readiness, and governance requirements.
Business & Leadership Capabilities
- Strong business acumen and the ability to connect technology decisions to operational value and revenue impact.
- Excellent communication skills with the ability to work effectively with business leaders, technical teams, and end users.
- Self-motivated, detail and process oriented, with strong follow-through and a passion for excellence.
- Strong facilitation, listening, and problem-solving skills.
- Ability to operate with urgency, pragmatism, and sound judgment in a fast-moving environment.
Preferred Qualifications
- Experience with agentic AI architecture, multi-step orchestration, and real-world implementation of agent-based solutions.
- Familiarity with change enablement, adoption strategy, and value realization for new productivity solutions.
- Experience creating reusable offerings, accelerators, or service-based solution patterns that can support commercial growth.
- Exposure to governance, security, and responsible AI considerations in enterprise environments.
Here’s What You’ll Get
- Highly competitive Medical, Dental, and Vision Insurance
- Flexible Spending or Health Savings Accounts
- Unlimited Vacation Time
- 10 Paid Holidays
- 12 Paid Weeks Birthing Parent Leave
- Pet Insurance
- Retirement Savings: 401(k) and Employee Stock Ownership Plan
- Employee Referral Bonus
- Professional Development Reimbursement
- Company Paid STD, LTD, and Life Insurance
Compensation: This position offers a base salary range of $130,000 to $150,000, based on experience and qualifications. The role is also eligible for a variable compensation plan.
The posted salary range represents a good faith estimate of the expected compensation for this role at the time of posting. Final compensation will be determined based on factors such as relevant experience, skills, qualifications, internal equity, and business needs.
On-Site Requirements
This position requires a full on-site presence 5 days per week during the first 4–6 months. Following that period, the role will be on-site four days per week, including Monday, Wednesday, and Thursday, with the fourth on-site day alternating between Tuesday or Friday depending on business needs.
Must be currently authorized to work in the United States.
Policy on Third-Party Unsolicited Resume Submissions: Please note that any third-party unsolicited resume submissions will immediately become the property of Stellix. Stellix will not pay any fee to a submitting employment agency, person, or entity unless a signed agreement is established.
Please Note: Stellix is an Equal Opportunity Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, gender identity, sexual orientation, national origin, disability, protected veteran status or any other characteristic protected by law.
Salary Context
This $130K-$150K range is below the median for AI/ML Engineer roles in our dataset (median: $175K across 1356 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 8,943 AI roles we're tracking, AI/ML Engineer positions make up 75% of the market. At Stellix, 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. Senior-level AI roles across all categories have a median of $228,700. This role's midpoint ($140K) sits 34% below the category median. Disclosed range: $130K to $150K.
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.
Stellix AI Hiring
Stellix has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Foxborough, MA, US. Compensation range: $150K - $150K.
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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