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About This Role
WHAT FLEXWARE DOES
Flexware Innovation is a leading technology integrator that helps forward thinkers in manufacturing and related industries build comprehensive and long-lasting solutions with ease.
Founded in 1996 and based in Indiana, our teams of talented engineers leverage technology to solve real business problems with teams of engineers focused on industrial controls, manufacturing systems integration, software development, business intelligence, and Internet of Things (IoT) devices. Our passion is helping our customers build solutions that stand the test of time by creating solid architecture and helping customers avoid costly design mistakes.
But we’re not just invested in technology – we’re also invested in people. Our internal promise to our Flexdogs is to have a positive and lasting impact on our families by providing a healthy and engaging work environment. Our environment is fun, family-friendly, energetic, and was nominated for TechPoint’s Mira Award for “Company Culture of the Year” in 2017, four Powderkeg awards in 2019, and 5 Powderkeg awards in 2022.
WHAT YOU WILL DO
We are seeking an exceptional Senior Manager Industrial AI to lead our Industrial AI consulting and implementation practice. Reporting to the Vice President of Digital Transformation and Industrial AI, this role combines strategic consulting leadership with deep technical expertise in AI applications for manufacturing and industrial operations. The successful candidate will drive business growth, shape service offerings, lead client engagements, manage a team of practitioners, and establish thought leadership in the industrial AI domain.
*Strategy & Service Development*
- Partner with the Vice President to define and evolve the industrial AI services strategy, aligned with Hitachi's AI vision and Flexware's Smart Manufacturing service portfolio
- Design industry-specific industrial AI service offerings in collaboration with vertical industry leads, addressing use cases such as predictive maintenance, quality optimization, process control, digital twins, and autonomous operations
- Identify market opportunities and competitive positioning for industrial AI services
- Evaluate and recommend industrial AI technology vendors and strategic partners
*Client Engagement & Delivery*
- Lead senior-level consulting engagements, including industrial AI strategy development, maturity assessments, roadmap creation, and business case development for manufacturing clients
- Direct industrial AI implementation projects encompassing data strategy, architecture design, technology selection, solution configuration, and deployment
- Build and maintain trusted advisor relationships with C-suite and senior operational leaders at client organizations
- Ensure delivery excellence, client satisfaction, and successful business outcomes across all engagements
*Business Development & Sales*
- Drive revenue growth through active pursuit of industrial AI opportunities with new and existing clients
- Lead proposal development, client presentations, and commercial negotiations for complex service engagements
- Build and manage sales pipeline, forecasting, and account planning for the industrial AI service line
*Team Leadership & Development*
- Manage, mentor, and develop a team of industrial AI consultants and technologists
- Establish quality standards, methodologies, and best practices for industrial AI engagements
- Manage project staffing, resource allocation, and team utilization
- Recruit and onboard talent to scale the industrial AI practice
*Financial Management*
- Manage revenue targets and profitability metrics for the industrial AI service line
- Develop pricing strategies and commercial models for consulting and implementation services
- Optimize service delivery efficiency and margin performance
*Thought Leadership & Market Presence*
- Develop and publish thought leadership content including whitepapers, case studies, and articles on industrial AI topics
- Represent Flexware at industry conferences, client events, and speaking engagements
- Contribute to marketing materials and intellectual capital that differentiates Flexware's industrial AI capabilities
*Collaboration & Innovation*
- Identify opportunities to evolve services into product or managed service offerings, collaborating with Flexware's product development and managed services teams
- Support industrial AI initiatives across JR Automation and Hitachi portfolio companies
- Build ecosystem partnerships with AI technology providers and complementary service firms
WHAT YOU MIGHT HAVE DONE BEFORE
*Education & Experience*
- Bachelor's degree in Engineering, Computer Science, Data Science, or related technical field; advanced degree (MS, MBA, PhD) preferred
- 10+ years of professional experience with a combination of management consulting, industrial AI/digital transformation implementation, manufacturing operations leadership, and industrial automation
- Minimum 5 years in management consulting, with experience at senior levels
- Minimum 5 years of hands-on experience with industrial automation, AI, machine learning, or advanced analytics in manufacturing or industrial operations settings
- Proven track record managing teams and delivering complex projects from strategy through implementation
*Technical Expertise*
- Deep understanding of industrial AI use cases including predictive maintenance, quality prediction, process optimization, digital twins, computer vision, and autonomous operations
- Knowledge of Industry 4.0 frameworks, smart manufacturing concepts, and digital transformation methodologies
- Knowledge of AI/ML technologies, platforms, and deployment architectures (edge computing, cloud, hybrid)
- Familiarity with industrial systems including SCADA, MES, historians, IoT platforms, and OT/IT integration
- Understanding of data engineering, MLOps, and model lifecycle management in production environments
*Industry Knowledge*
- Experience working with manufacturing or industrial operations clients across process, discrete, or hybrid environments
- Expert understanding of manufacturing operational challenges, KPIs, and value drivers
- Knowledge of operational technology (OT) and IT/OT integration challenges and solutions
- Familiarity with industrial automation systems including PLC, SCADA, DCS, MES, AMRs, and historians
*Consulting & Business Skills*
- Exceptional problem-structuring and analytical skills with ability to synthesize complex information
- Outstanding communication and presentation skills, with demonstrated ability to engage C-suite executives
- Strong business acumen including financial modeling, ROI analysis, and commercial strategy
- Proven ability to develop compelling proposals, business cases, and client-facing materials
- Track record of successful business development and client relationship management
*Leadership & Interpersonal*
- Experience building, managing, and developing high-performing teams
- Executive presence with ability to build credibility with senior stakeholders
- Collaborative mindset with proven ability to work across organizational boundaries
- Entrepreneurial orientation with comfort in ambiguous, rapidly evolving environments
- Strong project and program management capabilities
### Preferred Qualifications
- Experience with Hitachi technologies or in organizations with similar manufacturing/industrial focus
- Background in industrial engineering, process engineering, or manufacturing operations
- Certifications in AI/ML, data science, or project management (PMP, Lean Six Sigma, etc.)
- Published thought leadership or speaking experience at industry conferences
- Experience building or scaling service practices or consulting practices from early stages
- Prior P&L or business unit management responsibility
- Demonstrated proficiency leveraging generative AI tools to enhance productivity, including experience establishing best practices for responsible AI tool usage within professional services terms
### Work Environment & Travel
- This is a remote position with flexibility to work from anywhere within the continental United States
- Periodic travel to Flexware headquarters in Fishers, IN is expected
- Travel to client sites is expected at approximately 30-40% depending on project assignments
If you’re interested in this role, we’re excited to start a conversation with you! Please reach out to Lindsay Imhoff at [email protected]. Your inquiry and conversation will be treated with confidentiality, and we will not share your information with others.
*We are an Equal Opportunity Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, protected veteran status, or any other legally protected status.*
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 3,897 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Flexware Innovation, 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 $154,000 based on 8,743 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $225,000.
Across all AI roles, the market median is $190,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $300,688. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Safety ($274,200). By seniority level: Entry: $85,000; Mid: $147,000; Senior: $225,000; Director: $230,600; VP: $248,357.
Flexware Innovation AI Hiring
Flexware Innovation has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Remote, US.
Remote Work Context
Remote AI roles pay a median of $160,000 across 1,226 positions. About 16% 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 3,897 open positions tracked in our dataset. By seniority: 111 entry-level, 1,958 mid-level, 1,413 senior, and 415 leadership roles (Director, VP, C-Level). Remote roles make up 16% of the market (615 positions). The remaining 3,251 roles require on-site or hybrid attendance.
The market median for AI roles is $190,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $300,688. Highest-paying categories: AI Engineering Manager ($293,500 median, 21 roles); AI Safety ($274,200 median, 24 roles); Research Engineer ($260,000 median, 264 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 3,897 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,733), Data Scientist (273), AI Software Engineer (271). 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 (111) are outnumbered by mid-level (1,958) and senior (1,413) 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 415 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 16% of all AI roles (615 positions), with 3,251 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 $190,000. Top-quartile roles start at $244,000, and the 90th percentile reaches $300,688. 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 Engineering Manager roles lead at $293,500 median, while Prompt Engineer roles sit at $145,600. 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 (2,064 postings), Aws (1,085 postings), Azure (867 postings), Rag (865 postings), Gcp (697 postings), Pytorch (650 postings), Prompt Engineering (597 postings), Kubernetes (499 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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