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About This Role
### Job Description:
Shield AI is seeking a Director of AI Operations \& Governance to operationalize and govern our workplace AI ecosystem across all AI initiatives. Reporting to the VP of Workplace AI, this role will own license and platform operations, AI governance, security posture, and ongoing lifecycle management of AI tools that support Shield AI's business functions. The role will be the central owner of "post–dev\-ops" for AI, ensuring systems are reliable, compliant, secure, and continuously improving in line with production usage and business needs, while building and leading the team responsible for AI sustainment.
This role requires significant hands\-on technical capability across the machine learning and generative AI lifecycle — not just program oversight. The Director must be able to credibly evaluate, tune, and troubleshoot models and AI systems at a technical level in order to govern them effectively, partner with engineering, and make sound tradeoffs between reliability, performance, cost, and risk.
### What you'll do:
Technical AI/ML Ownership
- Evaluate, benchmark, fine\-tune, and adjust configurations of ML and generative AI models (including LLMs) in production, applying working knowledge of model training, tuning, and evaluation methodologies rather than relying solely on vendor documentation.
- Apply applied AI/ML research and emerging techniques to inform build\-vs\-buy decisions, model selection, and architecture choices across the AI portfolio.
- Partner directly with data science and ML engineering teams on model performance issues, drift detection, and retraining or reconfiguration needs, contributing technical judgment rather than acting purely as a liaison.
- Maintain technical fluency in prompt engineering, retrieval\-augmented generation, agentic/orchestration frameworks, and the practical distinctions between generative AI and traditional ML systems, and translate those distinctions into governance and staffing decisions.
AI Sustainment \& Governance
- Own AI sustainment and governance for workplace AI tools across enablement, bought solutions, and custom builds, acting as the central "run" function for the AI strategy, and building the team and processes to scale it.
- Manage all AI\-related licenses and entitlements: monitor usage, optimize allocations, drive reallocation, and partner with Finance for cost visibility and optimization.
- Monitor production usage patterns and performance to recommend roadmap items, enhancements, and deprecations based on real\-world dynamics in production.
- Own and triage support tickets for workplace AI tools and platforms, driving resolution across vendors, internal engineering, and security partners.
- Lead continuous evaluations of AI tools and models, including monitoring drift, benchmarking performance, and ensuring tools remain effective and aligned with business KPIs.
- Maintain the updates and security outlook for AI platforms, coordinating patches, version upgrades, vulnerability remediation, and compliance with Shield AI security policies.
- Orchestrate model swaps and configuration changes in production, including rollout planning, risk assessment, change control, and post\-deployment monitoring.
- Design, maintain, and govern shared prompt libraries, including standards for prompt quality, reuse, versioning, and training for end\-users and builders.
- Own management of secrets (API keys, credentials, tokens) used by AI tools and orchestrators, ensuring secure storage, rotation, and access control in partnership with Security and IT.
- Define and maintain connectors and extensions (e.g., integrations into SaaS systems, data sources, and workflow tools) to ensure reliable, secure data access for AI workflows.
- Establish and operate auditability frameworks for AI tools, including logging, traceability of AI\-assisted actions, and reporting for compliance and risk management.
- Lead AI governance practices for workplace AI (policies, guardrails, usage standards, approval workflows, exception processes) in partnership with Security, Legal, and HR.
- Partner with business solution and build teams to ensure their deliverables meet sustainment, observability, and governance requirements before moving to production.
- Define operational playbooks, SLAs, and incident response procedures for AI systems, including on\-call patterns supported by contractors and platform specialists.
Leadership \& Strategy
- Build, lead, and develop a team of AI operations professionals, contractors, and platform specialists, establishing career paths and scaling the function as the organization matures.
- Set the strategic direction for AI operations and governance, translating enterprise priorities into a multi\-quarter roadmap and budget owned by this role.
- Provide regular status and risk updates to the VP of Workplace AI and other senior leadership, including adoption metrics, reliability indicators, governance findings, and cost trends.
### Required qualifications:
- 15\+ years in platform operations, ML/AI operations, DevOps, or SaaS sustainment roles, including significant experience in leadership/people management, with a track record of running production systems in a high\-stakes environment (defense, aerospace, enterprise SaaS, or similar).
- Direct, hands\-on experience developing, training, fine\-tuning, or evaluating machine learning models or generative AI systems — this is a core requirement, not a nice\-to\-have. Candidates should be able to speak credibly to model architecture, training/tuning approaches, and evaluation methodology.
- Working knowledge of AI/ML research practices and the ability to apply current research to production decision\-making.
- Software engineering or data science background sufficient to engage deeply with technical teams on model behavior, integration issues, and system design tradeoffs.
- Direct experience with AI platforms or orchestration tools (e.g., LLM providers, RPA/workflow tools like n8n, enterprise SaaS integrations) and their operational management, including at an organizational or strategic level.
- Demonstrated understanding of the distinct technical and operational challenges of generative AI versus traditional ML — including differing skill requirements, risk profiles, and market/salary dynamics — and ability to apply that distinction to team design and hiring.
- Strong background in governance, compliance, or security in the context of data\-driven or AI systems, including familiarity with audit, logging, and access control best practices.
- Demonstrated ability to manage licenses and cost optimization for SaaS or AI tools at scale, including working with Finance and procurement stakeholders, and managing significant budgets.
- Hands\-on experience with monitoring and observability stacks (logs, metrics, alerts) and using those signals to shape product roadmaps and operational improvements.
- Strong technical fluency across APIs, connectors, and integrations; able to work closely with engineering and vendors to design and maintain extensions.
- Proven track record building and leading high\-performing teams, including hiring, mentoring, and developing talent across a mix of core staff and contractors.
- Excellent executive communication skills, with ability to translate production dynamics and risk into clear recommendations for senior business and technical leaders, including executive stakeholders.
- Experience operating in a hybrid environment of contractors and core team members, with the ability to define processes and standards that scale as the team matures.
$280,000 \- $420,000 a year
\#LF
We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses and identifying potential inconsistencies or verification signals in application materials based on available information. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.
Salary Context
This $280K-$420K range is above the 75th percentile 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
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 Shield AI, 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 $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 ($350K) sits 60% above the category median. Disclosed range: $280K to $420K.
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.
Shield AI AI Hiring
Shield AI has 5 open AI roles right now. They're hiring across AI/ML Engineer. Positions span Remote, US, Melbourne, FL, US. Compensation range: $420K - $420K.
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
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