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About This Role
Description:
We are seeking an AI Enablement Specialist responsible for enabling and scaling business\-led AI solutions across the organization. This role serves as the primary Tier 2 resource supporting business analysts and end users in the adoption, development, and optimization of AI\-powered tools including Microsoft Copilot, Copilot Studio Agents, Claude, and Microsoft Foundry.
This role combines strong business analysis and user enablement skills with hands\-on technical capabilities. The AI Enablement Specialist will work directly with business and product teams to identify opportunities, guide solution design, troubleshoot issues, establish best practices, and build AI agents that accelerate productivity while maintaining security and governance standards.
This position bridges business innovation and technical implementation, supporting a federated AI development model while ensuring solutions remain aligned with enterprise architecture, compliance requirements, data security expectations, and Responsible AI principles.
Requirements:
AI Solution Enablement \& User Support
- Serve as the primary Tier 2 escalation point for AI\-related support requests.
- Partner with business users and business analysts to identify opportunities for AI\-enabled automation and productivity improvements.
- Provide consultation on agent design, prompt design, data grounding, and AI solution architecture.
- Coach citizen developers and power users on AI development best practices.
- Create training materials, office hours, documentation, and knowledge articles.
- Assist teams in evaluating AI use cases for feasibility, risk, and expected value.
Agent Development \& Configuration
- Design, build, and maintain Microsoft Copilot Studio agents, Copilot extensions, and Claude\-based business solutions.
- Configure agent instructions, topics, actions, knowledge sources, and retrieval strategies.
- Develop light integrations between AI platforms and enterprise systems using APIs, Power Platform, connectors, and workflow automation tools.
- Support testing, deployment, and lifecycle management of AI solutions.
- Implement reusable patterns and templates that accelerate AI development across the organization.
AI Platform Administration \& Governance
- Monitor AI agent usage, adoption, effectiveness, and operational health.
- Troubleshoot issues involving prompts, grounding data, connectors, permissions, and integrations.
- Support governance activities including access reviews, approval processes, and policy adherence.
- Partner with Information Security, Legal, Risk, and Compliance teams to ensure AI solutions align with corporate standards.
- Assist in establishing and maintaining AI development standards and operating procedures.
Microsoft Foundry \& Advanced AI Services
- Support business teams evaluating advanced AI capabilities available through Microsoft Foundry.
- Assist with proof\-of\-concept development and experimentation.
- Help identify opportunities for AI orchestration, document intelligence, knowledge retrieval, and workflow automation.
- Coordinate with enterprise architecture, development teams or external consultants when advanced engineering support is required.
Cross\-Functional Collaboration
- Partner closely with Product Owners, Salesforce teams, IT Operations, Information Security, Project Delivery, and business stakeholders.
- Translate business requirements into scalable AI\-enabled solutions.
- Assist with roadmap planning and prioritization of AI enhancement requests.
- Stay current on emerging AI capabilities and recommend adoption opportunities to leadership.
Required Qualifications
AI \& Automation Experience
- 2\+ years of experience supporting, configuring, or implementing AI\-powered business solutions.
- Experience with Microsoft Copilot, Copilot Studio, Claude, Power Platform, or comparable AI platforms.
- Understanding of prompt engineering, retrieval\-based AI systems, and agent design concepts.
- Experience supporting end users and troubleshooting production issues.
Technical Skills
- 4\+ years of experience designing, building, supporting, and enhancing business software applications, integrations, or enterprise technology solutions.
- Experience building workflows and automations using Power Automate, Power Platform, or similar technologies.
- Working knowledge of REST APIs, JSON, authentication concepts, and modern SaaS integrations.
- Familiarity with Microsoft 365, SharePoint, Teams, and enterprise data sources.
- Ability to configure connectors and light integrations without requiring full software engineering support.
Business \& Consulting Skills
- Strong requirements gathering and business analysis capabilities.
- Ability to explain technical concepts to non\-technical audiences.
- Experience facilitating workshops, training sessions, and user adoption initiatives.
- Strong documentation and process design skills.
Preferred Qualifications
- Experience with Microsoft Copilot Studio agent development and lifecycle management.
- Experience with Azure AI Foundry or Microsoft Foundry capabilities.
- Experience with Salesforce, ServiceNow, Microsoft Power Platform, or other enterprise business platforms.
- Power Platform, Microsoft AI, Azure AI, or related technology certifications.
- Experience working in regulated industries such as banking, financial services, mortgage, lending, or insurance.
Soft Skills
- Strong communication, facilitation, analytical, and consulting skills.
- Ability to work directly with users, ask effective discovery questions, and translate business needs into practical AI solutions.
- Comfortable balancing user enablement, hands\-on technical build, governance expectations, and support responsibilities.
- Curious, adaptable, and able to learn emerging AI capabilities independently.
- Ownership mindset with a willingness to engage in both strategic enablement and tactical support work.
- Strong judgment around security, data sensitivity, policy adherence, and when to escalate to technical, risk, or compliance partners.
Our Benefits: Health, Vision, Dental, 401K, ESOP, 100% Tuition Assistance, 4 weeks paid time off, plus a few more
About Merchants
Ranked as a top performing U.S. public bank by S\&P Global Market Intelligence, Merchants Bancorp is a diversified bank holding company headquartered in Carmel, Indiana operating multiple segments, including Multi\-family Mortgage Banking that offers multi\-family housing and healthcare facility financing and servicing; Mortgage Warehousing that offers mortgage warehouse financing; and Banking that offers retail and correspondent residential mortgage banking, agricultural lending, and traditional community banking. Merchants Bancorp, with $18\.8 billion in assets and $11\.9 billion in deposits as of December 31, 2024, conducts its business primarily through its direct and indirect subsidiaries, Merchants Bank of Indiana, Merchants Capital Corp., Merchants Capital Investments, LLC, Merchants Capital Servicing, LLC, Merchants Asset Management, LLC, and Merchants Mortgage, a division of Merchants Bank of Indiana.
Merchants Bank and Merchants Capital have recently been honored with the 2025 USA Today Top Workplaces recognition, ranking 22nd nationally within the 500\-999 employee category. This is the second year that Merchants has been recognized with this award. These accolades build on our strong history of workplace recognition, including being named a Best Place to Work in Indiana for seven consecutive years (2016–2022\). For more information read the entire article here.
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 Merchants Bank of Indiana, 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.
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.
Merchants Bank of Indiana AI Hiring
Merchants Bank of Indiana has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Carmel, IN, US.
Location Context
Across all AI roles, 15% (635 positions) offer remote work, while 3,657 require on-site attendance. Top AI hiring metros: New York (1,650 roles, $220,000 median); San Francisco (1,335 roles, $265,000 median); Los Angeles (708 roles, $214,112 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 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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