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About This Role
adroitts is a fast\-growing IT solutions company that helps businesses adapt and grow in a continuously evolving market. Our tailor\-made technological solutions are perfectly aligned to our client’s business goals and objectives. we strive to be a long\-term trusted and reliable partner for our customers organization to help overcome IT challenges. Our solutions, methodologies and implementations are designed with customer centric focus and customers ROI. We pride ourselves in our employees feeling welcomed, valued, and involved.
This is a hands\-on operational role responsible for the day\-to\-day deployment, administration, security integration, and governance Claude Enterprise and ChatGPT Enterprise tenants. This is not an architecture or advisory position. The successful candidate will own platform operations from day one — tenant configuration, user lifecycle, connector governance, audit log pipelines, incident response, and ongoing security controls under a CISO and CIO in a regulated enterprise environment. No PHI is processed, stored, or permitted in any AI platform
TECHNOLOGY STACK — CANDIDATE MUST OPERATE IN
Claude Enterprise
Primary AI platform — full tenant administration
ChatGPT Enterprise
Secondary AI platform — full tenant administration
Okta
Identity provider — SSO enforcement \& user lifecycle
SCIM
Automated provisioning \& deprovisioning to AI platforms
CyberArk
PAM — API key vaulting \& credential rotation
Splunk
Audit log ingestion, monitoring \& anomaly detection
Microsoft 365
Current connector ecosystem — SharePoint, Teams, Outlook
ServiceNow
Ticketing \& change control for platform requests
HARD REQUIREMENTS — NON\-NEGOTIABLE
Every item below must be demonstrated with prior hands\-on experience. Familiarity, exposure, or architectural knowledge is not sufficient. Candidates will be asked to walk through specific deployments in detail during the interview process.
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Claude Enterprise \& ChatGPT Enterprise — hands\-on tenant administration
Tenant setup, organizational policy configuration, feature controls, and ongoing production ownership. Must demonstrate prior tenant\-level administration — not API usage or model development.
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Okta SSO \& SCIM — enterprise AI platform provisioning
End\-to\-end automated user provisioning and deprovisioning workflows integrated directly with an enterprise AI platform. General Okta experience alone is not sufficient.
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CyberArk — privileged access \& secrets management
API key vaulting, credential rotation, and service account governance specifically for AI platform integrations and connectors.
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Splunk — AI platform audit log pipeline \& monitoring
Pipeline design, log ingestion, parsing, dashboard creation, and anomaly detection tied specifically to AI platform activity. General SIEM experience is not sufficient.
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Enterprise AI platform connector governance \& operations
Administration, permission scoping, security review, change control, and operational monitoring of connectors within Claude Enterprise and ChatGPT Enterprise — including current and future integrations across any enterprise system. The connector ecosystem will grow and the candidate must be able to govern and operate new connectors as they are introduced.
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Enterprise AI platform security controls
Data classification enforcement, acceptable use policy controls, access recertification, and audit evidence maintenance at the platform level.
PREFERRED QUALIFICATIONS
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Healthcare or payer industry background
Familiarity with the regulatory and data sensitivity environment of a national health insurance organization and the governance expectations that come with it.
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Enterprise SaaS platform operations at scale
Prior experience owning a governed SaaS platform under a CISO — including change control, executive reporting, and formal audit support.
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Acceptable use policy enforcement
Hands\-on experience detecting, investigating, and responding to user policy violations within an enterprise AI platform — not just writing the policy.
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Platform usage analytics \& executive reporting
Ability to produce monthly engagement metrics, user adoption trends, and security event summaries for CISO and CIO audiences from audit log data.
CORE OPERATIONAL RESPONSIBILITIES
Platform administration
Daily tenant management, configuration control, feature governance, and policy enforcement across both AI platforms
User lifecycle
Provisioning, deprovisioning, access recertification, and role management via Okta and SCIM
Incident response
First responder for platform outages, connector failures, SSO issues, and security events
Connector operations
Governance, monitoring, and change control for all current and future enterprise system connectors across both AI platforms
Security monitoring
Splunk pipeline ownership, anomaly detection, policy violation response, and audit log integrity
Executive reporting
Monthly usage metrics, engagement analytics, and security event summaries for CISO and CIO
WHAT THIS ROLE IS NOT
This is not an AI engineering, ML, or model development role. Candidates whose primary experience is building RAG pipelines, fine\-tuning models, designing AI architectures, or advising on AI strategy are not the right profile — even if they have incidental Claude or ChatGPT Enterprise exposure. This role requires someone whose primary professional identity is platform operations and security integration, not AI engineering or architecture.
REPORTING STRUCTURE
Reports to Senior Information Security \& Technology leadership. Works closely with CISO and CIO. Operates as an individual contributor with broad platform ownership and no direct reports. Must be comfortable working in a structured governance environment with formal change control, CAB approval processes, and executive\-level reporting requirements.
We are looking forward to hearing from you!
Best,
adroitts
Pay: $70\.00 \- $80\.00 per hour
Work Location: Remote
Salary Context
This $145K-$166K range is below the median for AI/ML Engineer roles in our dataset (median: $180K across 2130 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 4,133 AI roles we're tracking, AI/ML Engineer positions make up 69% of the market. At adroitts, 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 $185,000 based on 13,200 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $165,778. This role's midpoint ($156K) sits 16% below the category median. Disclosed range: $145K to $166K.
Across all AI roles, the market median is $200,700. Top-quartile compensation starts at $254,000. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Safety ($274,200) and AI Engineering Manager ($268,700). By seniority level: Entry: $97,760; Mid: $165,778; Senior: $227,400; Director: $250,000; VP: $250,000.
adroitts AI Hiring
adroitts has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Remote, US. Compensation range: $166K - $166K.
Remote Work Context
Remote AI roles pay a median of $173,300 across 2,012 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 4,133 open positions tracked in our dataset. By seniority: 106 entry-level, 1,901 mid-level, 1,663 senior, and 463 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (583 positions). The remaining 3,532 roles require on-site or hybrid attendance.
The market median for AI roles is $200,700. Top-quartile compensation starts at $254,000. The 90th percentile reaches $307,500. Highest-paying categories: AI Safety ($274,200 median, 57 roles); AI Engineering Manager ($268,700 median, 42 roles); Research Engineer ($260,000 median, 442 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 4,133 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,865), Data Scientist (339), AI Software Engineer (313). 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 (106) are outnumbered by mid-level (1,901) and senior (1,663) 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 463 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (583 positions), with 3,532 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 $200,700. Top-quartile roles start at $254,000, 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 Safety roles lead at $274,200 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 (2,128 postings), Aws (1,324 postings), Azure (1,003 postings), Rag (916 postings), Gcp (817 postings), Pytorch (655 postings), Prompt Engineering (639 postings), Claude (571 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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