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About This Role
We're looking for an experienced and hands\-on Agentic Enterprise Engineer to join our AI innovation team. In this role, you'll design and build intelligent, agentic workflows that improve how business processes are executed. You'll partner with business process owners, system administrators, and AI innovation teammates to understand automation needs, develop practical solutions using modern AI agent frameworks, and contribute to measurable productivity and efficiency gains within defined project priorities.
Each engagement is connected to business value and implementation goals, with success defined by delivering reliable, usable solutions. Once implemented, solutions are transitioned to the appropriate business teams with supporting documentation, training materials, and handoff guidance.
This is a visible, hands\-on role for someone who thrives at the intersection of enterprise systems, AI, and practical business process improvement.What You'll Do
- Support Discovery Sessions: Partner with business process owners and cross\-functional stakeholders to identify, clarify, and scope automation opportunities with clear business value.
- Design \& Build Agentic Workflows: Design and implement intelligent, multi\-step agentic automation solutions using tools such as Microsoft Copilot Agents, Agent Foundry, Copilot Studio, Atlassian Rovo, Claude, and GitHub Copilot Agents, with guidance on architecture and standards from senior AI technology leaders.
- Drive Documentation, Training \& Change Management: Ensure every solution comes with the documentation, user training, and change management plan needed for a successful handover to the business line.
- Stay Current on AI Automation: Keep informed on the evolving AI automation landscape and share relevant tools, frameworks, and practices that may improve project delivery.
- Follow AI Governance Processes: Apply Merative's AI Governance processes and support preparation of materials needed for responsible, compliant review and deployment of AI solutions.
- Collaborate on Security \& Operations: Work with CISO, IT Operations, and AI CoE partners to ensure agentic solutions align with established security, cost management, and operational standards.
- Contribute to Reusable Patterns: Document lessons learned, implementation approaches, and reusable components that can improve quality and efficiency across future automation projects.
What You'll Need (Required Qualifications)
- 5–10 years of experience designing and implementing enterprise back\-end systems.
- 2\+ years of experience in workflow automation, process automation, or systems integration.
- 2\+ years of hands\-on experience applying modern agentic AI automation in production or near\-production environments.
- Demonstrated experience designing and deploying Microsoft Copilot Agents (including Agent Foundry and Copilot Studio).
- Strong working knowledge of the Microsoft M365 suite, Microsoft Azure Cloud, Microsoft Fabric, and Microsoft Lake services.
- Ability to translate technical capabilities into clear business value and communicate effectively with both technical and non\-technical stakeholders.
- Strong communication and presentation skills, with the ability to clearly explain technical work, project status, and business value to technical and non\-technical audiences.
- A collaborative, team\-first mindset with a proven track record of working across functions and business lines.
Nice to Have (Preferred Qualifications)
- Experience with Atlassian tools including Jira and Confluence for project tracking and documentation.
- Familiarity with GitHub and GitHub Copilot Agents for code\-based automation and collaboration.
- Experience with AtlassianRovo or similar enterprise AI assistants.
- Exposure to Claude (Anthropic) or other LLM\-based agent platforms.
- Experience working within formal AI governance or compliance frameworks.
- Background in healthcare technology, data analytics, or regulated industries.
Compensation
The salary range provided in this job posting is intended to reflect the general market value for the position. The actual salary offered may vary based on factors such as the candidate’s experience, qualifications, skills, and the specific requirements of the role. This range may also be subject to change as market conditions evolve. We encourage open communication throughout the interview process to discuss compensation expectations. For base\-salary \+ commission sales roles, the range represents On\-Target Earnings.
Min – Max :
$152,440\.00 \- $228,660\.00 (USD) Benefits
The benefits described represent the current offerings at our organization, however, benefits are subject to change and may vary by location and employment status. We strive to provide a comprehensive benefits package that supports our employees’ health, wellness, and financial goals. Please note that benefits may be discussed in more detail during the hiring process.
- Remote first / work from home culture
- Flexible vacation to help you rest, recharge, and connect with loved ones
- Paid leave benefits
- Health, dental, and vision insurance
- 401k retirement savings plan
- Infertility benefits
- Tuition reimbursement, life insurance, EAP – and more!
*It is the policy of Merative to provide equal employment opportunity (EEO) to all persons regardless of age, color, national origin, citizenship status, physical or mental disability, race, religion, creed, gender, sex, sexual orientation, gender identity and/or expression, genetic information, marital status, status with regard to public assistance, veteran status, or any other characteristic protected by federal, state or local law. In addition, Merative will provide reasonable accommodations for qualified individuals with disabilities.*
*Merative participates in the federal E\-Verify program to confirm the identity and employment authorization of all newly hired employees. For further information about the E\-Verify program, please click here:* *http://www.uscis.gov/e\-verify/employees*
Salary Context
This $152K-$228K range is above the median for AI/ML Engineer roles in our dataset (median: $175K across 2162 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 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Merative, 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 $214,900 based on 6,420 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $194,400. This role's midpoint ($190K) sits 11% below the category median. Disclosed range: $152K to $228K.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
Merative AI Hiring
Merative has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Remote, US. Compensation range: $228K - $228K.
Remote Work Context
Remote AI roles pay a median of $180,000 across 1,196 positions. About 15% 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 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 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 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). 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 (138) are outnumbered by mid-level (2,071) and senior (1,655) 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 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 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 $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. 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 $287,500 median, while Prompt Engineer roles sit at $145,000. 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 (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 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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