Interested in this AI/ML Engineer role at Infomagnus?
Apply Now →About This Role
About Us:
We are technologists, analysts, and designers solving challenges, together.
At InfoMagnus, we believe in balance and provide our people complete autonomy to do their best work, while creating an environment that awards personal and professional success.
Our daily work is not just a means to an end, it’s an extension… the beginning of a reward that continues to deliver results on current and future projects.
Our people enjoy creating solutions to complex problems.
We believe in creating connections that last, while generating gratitude that reciprocates to the clients we work with. InfoMagnus is an equal opportunity employer.Senior Director, AI Strategy.
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#### About The Roll:
Build and lead an AI Strategy practice from zero to scale. Own how companies identify, prove, and scale AI\-driven business value. Shape a new category: AI Value Realization \& Accountability.
At InfoMagnus, you won't inherit a practice. You'll build the team, the methodology and the market position.
Why This Role Exists:
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Most AI initiatives fail to deliver measurable business impact. Not because the models don't work, but because:
- No one owns whether AI actually delivered business outcomes
- Value is not rigorously defined or measured
- Organizations don't adopt new ways of working
### We built our Precision AI Framework to solve this.
This role owns two of its core disciplines:
- Strategy \& Enablement: where value comes from
- Value Measurement: how value is proven
#### The Practice You Will Build
You will recruit and lead a multidisciplinary team of strategy, value measurement, and change enablement consultants Your practice ensures every engagement begins with validated business value, drives the organizational change required to capture it, and ends with measurable proof of impact.
You don't need to do every role. You need to build and lead the system.
##### What You'll Do
Lead Strategic Engagements:
- Serve as the senior strategic leader on client engagements: lead discovery, frame opportunities, guide the team through delivery
- Drive AI opportunity qualification directly with executive stakeholders
- Champion workflow redesign and enablement. Ensure initiatives transform how work gets done, not just what tools people use
- Partner with client Finance on value metrics and confidence levels
Build the Practice:
- Recruit, mentor, and lead a team across strategy, measurement, and change management
- Own and evolve the practice methodologies
- Develop productized IP: opportunity assessments, change readiness models, enablement curricula, and measurement frameworks
- Drive IP toward recurring revenue through standardized assessments, dashboards, and accountability platforms
Shape the Market Narrative:
- Represent the firm's strategic capability to clients, partners, and the market
- Lead proposals, client presentations, and executive relationship building
- Build thought leadership through whitepapers, case studies, and speaking engagements
What You Bring (Required):
- 10\+ years in management consulting, enterprise strategy, or technology advisory, leading multidisciplinary teams and owning executive client relationships
- Track record leading AI, digital transformation, or strategy engagements for enterprise clients. Owning outcomes, not just contributing.
- Experience building or significantly growing a consulting practice or service line
- Strong executive presence and communication: C\-suite presentations, Finance partnership, business\-technical translation
- Experience with business process transformation and optimization
- Sufficient fluency in organizational change management to lead enablement specialists and represent enablement credibly to clients
- Experience helping clients envision and design new business solutions, products, or operating models, translating strategic opportunities into concrete concepts that teams can build, measure, and scale
- Experience defining business outcomes, selecting KPIs, and framing ROI for executive and Finance audiences as part of strategy or transformation engagements. You should be comfortable determining what to measure and why; the measurement team will own the statistical rigor underneath
Preferred:
- Experience leading AI adoption strategy at the program level: selecting use cases, sequencing investments, and designing how AI capabilities (assistive tools, intelligent automation, or autonomous agents) integrate into enterprise operations
- Experience driving organizational change and technology adoption at scale, including stakeholder alignment, behavior change, and measurable adoption outcomes
- Experience productizing consulting IP into repeatable assessments or SaaS offerings
- Familiarity with developer productivity landscape (GitHub Copilot, AI coding tools, DORA/SPACE metrics)
Why InfoMagnus:
- Build a practice from the ground up: team, methodology, and market position
- Stay hands\-on in strategic client work, not just managing delivery
- Fortune 500 client base, including Microsoft, Southwest Airlines, Dell, Arm, and others
- Deep Industry Partnerships with strong position in developer productivity
- Opportunity to create high\-value IP and recurring revenue streams
- Integrated AI framework spanning the full lifecycle, not point solutions
Compensation \& Benefits: Senior Director\-level compensation structured for practice leadership and business impact:
- Base salary commensurate with Senior Director level in applied AI consulting
- Performance incentives tied to practice growth, client outcomes, and IP development
- Comprehensive health, dental, vision; 401(k); flexible PTO
This Role Is For You If:
- You want to build, not inherit
- You think AI success should be measured in business outcomes, not pilots
- You're as comfortable with executive strategy as you are with operational reality
If you've been looking for the opportunity to build a practice around how AI actually delivers value, let's talk. Apply directly or reach out for a confidential discussion.
InfoMagnus is an equal opportunity employer.
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 3,823 AI roles we're tracking, AI/ML Engineer positions make up 69% of the market. At Infomagnus, 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 in Demand for This Role
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 $181,170 based on 12,692 positions with disclosed compensation. Director-level AI roles across all categories have a median of $247,800.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
Infomagnus AI Hiring
Infomagnus has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Huntington Beach, CA, US.
Location Context
Across all AI roles, 15% (590 positions) offer remote work, while 3,217 require on-site attendance. Top AI hiring metros: New York (2,643 roles, $211,000 median); San Francisco (2,168 roles, $253,000 median); Los Angeles (1,792 roles, $191,580 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 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 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 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). 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 (112) are outnumbered by mid-level (1,798) and senior (1,516) 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 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 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 $200,100. Top-quartile roles start at $253,500, and the 90th percentile reaches $307,500. 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 $275,000 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 (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 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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