Director, Business Process Analysis AI

$156K - $210K Remote Mid Level AI/ML Engineer

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Skills & Technologies

AnthropicClaude

About This Role

AI job market dashboard showing open roles by category

At Ensono, our Purpose is to be a relentless ally, disrupting the status quo and unleashing our clients to Do Great Things*!* We enable our clients to achieve key business outcomes that reshape how our world runs. As an expert technology adviser and managed service provider with cross\-platform certifications, Ensono empowers our clients to keep up with continuous change and embrace innovation.

We can Do Great Things because we have great Associates. The Ensono Core Values unify our diverse talents and are woven into how we do business. These five traits are the key to achieving our purpose. Honesty – Reliability – Curiosity – Collaboration – Passion

About the role and what you’ll be doing: The Director, AI Solutions \& Engagement leads discovery and engagement for Finance AI Transformation. While the engineering function builds and releases, this role identifies what to release, sources best practices and expertise, and brings AI into existing corporate\-function workflows without friction.

This is a process engineering role at its core. Successful candidates have mapped real business workflows, identified bottlenecks, and re\-engineered processes end\-to\-end. They think in systems, not features. They can sit down with a Finance director or a Procurement analyst, walk through how work actually happens today, and pinpoint the intervention points where AI delivers measurable value—not where it sounds clever.

The role requires high EQ. People across corporate functions may be uncomfortable with AI. The Director is the trusted voice who listens, asks the right questions, and brings the right solution back without making anyone feel replaced. This role is the front door of the function: opportunities you surface feed directly into delivery, and patterns you document become reusable assets across Ensono.

What You Will Do:

  • Lead discovery conversations across corporate functions — Finance, Sales Operations, Procurement, HR, and beyond. Interview stakeholders, listen for friction, surface the workflows where time and money are leaking.
  • Map and re\-engineer processes end\-to\-end — current\-state to future\-state, with the AI intervention points called out. Value stream thinking, not feature thinking. The function’s load\-bearing claim is that processes get re\-engineered, not just augmented — this role makes that claim true.
  • Triage opportunities ruthlessly — distinguish AI\-suited workflows from manual\-better ones, distinguish high\-value from low\-impact, distinguish scope from noise. Not everything worth doing is worth doing first.
  • Brief the engineering function with clean technical requirements — translate what you heard into the right solution form (a Claude skill, an application, a workflow integration, a process change with no software at all). Route opportunities to leadership when scope exceeds the function’s capacity.
  • Mentor and train license holders — run workshops and 1:1s with the corporate\-function users who have Claude access. Raise the floor on AI literacy. Not everyone self\-directs; some need a guide.
  • Maintain the discovery pipeline as a managed asset — opportunity backlog, status, ROI estimates, hand\-off readiness. The pipeline is data, not just notes.
  • Partner with Finance on the displaced\-spend ledger — translate process improvements into measurable financial outcomes. The function’s credibility depends on showing the dollars.
  • Be the high\-EQ contact point with stakeholders — non\-technical people learn to trust this role, so AI gets brought to them on their terms, not on technology’s terms.

We want all new Associates to succeed in their roles at Ensono. That’s why we’ve outlined the job requirements below. To be considered for this role, it’s important that you meet all Required Qualifications. If you do not meet all of the Preferred Qualifications, we still encourage you to apply.

Required Qualifications

  • Bachelor’s degree, or equivalent demonstrated experience in lieu of a degree. Background working at or with managed service providers, cloud / IT services firms, or enterprise SaaS / professional services organizations is strongly preferred — track record matters more than credentials.
  • Workflow re\-engineering experience — you have mapped, analyzed, and redesigned real business workflows end\-to\-end in a professional context. Consulting, internal transformation, BA work, service delivery design, or operations leadership in a tech / IT services environment.
  • End\-to\-end systems thinking — comfortable holding a full workflow (e.g., Solution\-to\-Cash, Procure\-to\-Pay, Hire\-to\-Retire) in your head, seeing where work hands off between teams, where data flow breaks, where decisions slow down. You think in flows, not steps.
  • Strong critical thinking — you challenge assumptions, ask second\-order questions, resist surface fixes, and can spot when a stakeholder is solving the wrong problem.
  • Strong written and verbal communication — you can talk credibly with both senior executives and individual contributors, and translate what you hear in one room into what gets heard in another.
  • Self\-directed time management — you manage your own stakeholder backlog, run your own calendar, and prioritize without being told what to work on next.
  • High EQ — you earn trust quickly with non\-technical stakeholders, can sit in a room with someone resistant to AI and bring them along, and read the unspoken signals in a discovery conversation.
  • Demonstrated experience using a generative AI tool (Claude, ChatGPT, Copilot, or equivalent) for real work — beyond casual chat. You can speak credibly to what AI can and can’t do, where it accelerates work, and where it fails.
  • Bias toward releasing and driving adoption — you find the smallest valuable intervention and get it built, rather than optimizing the discovery cycle forever.

Preferred Qualifications

  • Background in Business Analyst, Project Management, Consulting, Chief of Staff, or similar cross\-functional roles that required holding the full picture of a business problem.
  • MSP\-side experience — Customer Success, Professional Services delivery, Solutions Engineering, Account Management, or service delivery leadership at a managed service provider, cloud platform, or IT services firm. You understand how services get sold, scoped, delivered, and billed in our industry.
  • Experience with process modeling — workflow diagrams, swim\-lane diagrams, BPMN, RACI charts.
  • Familiarity with the Anthropic Claude API, Model Context Protocol (MCP), agentic tool design, or multi\-step agent workflows — you’ll work alongside engineers, and knowing what’s technically possible sharpens your discovery instincts.
  • Experience in Finance, Procurement, Sales Operations, HR, or other corporate functions — so you can credibly discuss workflows with practitioners on their own terms.
  • Familiarity with FinOps practices, cost\-aware engineering, displaced\-spend modeling, or per\-use\-case spend tracking.
  • Background in change management or organizational transformation — bringing teams through a workflow change is half the work.
  • Experience designing or running training programs — workshops, 1:1 mentoring, learning communities.
  • Comfort with structured outputs and data — Excel fluency, basic SQL, exposure to dashboards or BI tools. You don’t need to be technical, but you should be able to validate a number when someone hands you one.

Why Ensono?

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Ensono is a place to make better happen – for our clients and for your career. You can do great things through innovation or collaboration, by learning or volunteering, or to promote diversity and inclusion. You can do great things for your own health or for a healthier planet. Whatever it means to you to do great things we want Ensono to be the place you can do it.

We are a client\-facing business, but we do encourage clients to allow us to work remotely most of the time so if you are not required to be on a client site, you can choose to work from home or in our Ensono offices.

Some of our benefits include:

  • Unlimited Paid Days Off
  • Three health plan options through Blue Cross Blue Shield
  • 401k with company match
  • Eligibility for dental, vision, short and long\-term disability, life and AD\&D coverage, and flexible spending accounts
  • Paid Maternity Leave, Paternity Leave, and Sabbatical Leave
  • Education Reimbursement, Student Loan Assistance or 529 College Funding
  • Enhanced fertility coverage
  • Wellness program
  • Flexible work schedule
  • Depending on location, ability to take advantage of fitness centers

As of the date of this posting, a good faith estimate of the current pay scale for this role is $156,000 to $210,000 annually based on a full\-time schedule. Please note that placement in the range may vary based on numerous factors including but not limited to skills, experience, internal equity, and business needs. In addition to base salary, other compensation programs, depending on eligibility, include an annual bonus plan based on company and individual performance and an equity grant under our Associate Equity Appreciation Program.

Ensono is an Equal Opportunity/Affirmative Action employer. We are committed to providing equal employment to our Associates and building a diverse and inclusive workforce. All qualified applicants will be considered without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, protected veteran status, disability, or other legally protected basis, in accordance with applicable law.

Pay transparency nondiscrimination statement/posting OFCCP’s pay transparency policy can be found on OFCCP’s website.

If you need accommodation at any point during the application or interview process, please let your recruiter know or email USTalentAcquisition@ensono.com.

Salary Context

This $156K-$210K range is above the median 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

Company Ensono
Title Director, Business Process Analysis AI
Location Remote, US
Category AI/ML Engineer
Experience Mid Level
Salary $156K - $210K
Remote Yes

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 Ensono, 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

Anthropic (6% of roles) Claude (13% of roles)

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. Director-level AI roles across all categories have a median of $272,150. This role's midpoint ($183K) sits 16% below the category median. Disclosed range: $156K to $210K.

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.

Ensono AI Hiring

Ensono has 2 open AI roles right now. They're hiring across AI/ML Engineer. Based in Remote, US. Compensation range: $138K - $210K.

Remote Work Context

Remote AI roles pay a median of $185,334 across 717 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 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

Based on 3,817 roles with disclosed compensation, the median salary for AI/ML Engineer positions is $218,750. Actual compensation varies by seniority, location, and company stage.
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.
About 14% of the 3,708 AI roles we track offer remote work. Remote availability varies by company and seniority level, with senior and leadership roles more likely to offer location flexibility.
Ensono is among the companies actively hiring for AI and ML talent. Check our company profiles for detailed breakdowns of open roles, salary ranges, and hiring trends.
Common next steps from AI/ML Engineer positions include ML Architect, AI Engineering Manager, Principal ML Engineer. Progression depends on whether you lean toward technical depth, people management, or product strategy.

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