Interested in this AI/ML Engineer role at Information Technology Senior Management Forum?
Apply Now →About This Role
Posted Date
5/27/2026
Description
We are seeking a senior technology leader to build and scale a developer Platform (IDP) with a strong focus on AI\-driven test environment management. As Director, you will own the enterprise strategy, governance, and operational integrity of all non\-production environments (Dev, QA, UAT, Performance, Pre\-Prod) in a highly regulated financial services environment.
You will drive a platform\-as\-a\-product mindset, enabling self\-service capabilities, improving developer experience (DevEx), and leveraging Agentic AI to automate the lifecycle of test environments. This role is critical in accelerating software delivery while maintaining strict compliance, security, and operational resilience standards.
Key Responsibilities
Platform \& Architecture Leadership
- Architect and evolve the Internal Developer Platform (IDP) with self\-service, secure\-by\-default, and observable capabilities
- Build Test Environment as a Service (TEaaS) for on\-demand, production\-like environments
- Establish platform SLAs, adoption metrics, and continuous feedback mechanisms
AI\-Driven Environment Management
- Design and implement Agentic AI solutions to automate provisioning, scaling, optimization, and retirement of test environments
- Enable intelligent scheduling, reuse, and cost\-aware scaling based on demand
- Ensure AI actions are auditable, policy\-bound, and compliant
Environment Strategy \& Governance
- Define enterprise\-wide strategy for lower environment management across cloud, on\-prem, and hybrid ecosystems
- Establish standards for provisioning, configuration, refresh cycles, and decommissioning
- Ensure compliance with regulatory, security, and audit requirements
Operational Excellence
- Lead global engineering teams managing environment provisioning, stability, and support
- Implement observability, incident management, and reliability practices
- Reduce risks related to configuration drift, environment inconsistency, and manual processes
DevOps Enablement
- Partner with Engineering, QA, and Release teams to enable CI/CD pipelines and automated provisioning
- Remove environment bottlenecks to support parallel development and testing at scale
- Drive test data strategy, including synthetic data generation using AI
Cloud \& Cost Optimization
- Promote cloud\-native architectures and Infrastructure\-as\-Code (IaC)
- Optimize utilization through lifecycle automation, right\-sizing, and environment reuse
- Balance self\-service autonomy with governance and operational control
Leadership \& Stakeholder Management
- Lead and develop high\-performing global teams (FTE \+ vendors)
- Define workforce strategy, career development, and performance management
- Serve as the senior point of accountability for environment readiness and risk
- Partner with Architecture, Security, Risk, and Business stakeholders
Qualifications
Required
- 15\+ years of experience in platform engineering, DevOps, infrastructure, or technology operations
- 5\+ years leading enterprise\-scale engineering teams
- Proven expertise in managing development and test environments at scale
- Strong experience with cloud platforms, automation, and CI/CD ecosystems
- Experience operating in regulated environments (financial services preferred)
Preferred
- Experience building Internal Developer Platforms (IDP) or platform engineering models
- Exposure to AI/ML or Agentic AI implementations in operations
- Knowledge of test data management and synthetic data generation
- Master’s degree in Engineering, Computer Science, or related field
What You’ll Bring
- Strategic leadership with hands\-on technical depth
- Strong focus on developer experience, automation, and innovation
- Ability to balance speed, cost, and risk in complex enterprise environments
- Track record of driving transformation through modern engineering practices (Agile, DevOps, AI)
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#### Job Family Group:
Technology
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#### Job Family:
Applications Development
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#### Time Type:
Full time
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#### Primary Location:
Irving Texas United States
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#### Primary Location Full Time Salary Range:
$170,000\.00 \- $300,000\.00
In addition to salary, Citi’s offerings may also include, for eligible employees, discretionary and formulaic incentive and retention awards. Citi offers competitive employee benefits, including: medical, dental \& vision coverage; 401(k); life, accident, and disability insurance; and wellness programs. Citi also offers paid time off packages, including planned time off (vacation), unplanned time off (sick leave), and paid holidays. For additional information regarding Citi employee benefits, please visit citibenefits.com. Available offerings may vary by jurisdiction, job level, and date of hire.
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#### Most Relevant Skills
Please see the requirements listed above.
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#### Other Relevant Skills
For complementary skills, please see above and/or contact the recruiter.
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#### Anticipated Posting Close Date:
Jun 02, 2026
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*Citi is an equal opportunity employer, and qualified candidates will receive consideration without regard to their race, color, religion, sex, sexual orientation, gender identity, national origin, disability, status as a protected veteran, or any other characteristic protected by law.*
*If you are a person with a disability and need a reasonable accommodation to use our search tools and/or apply for a career opportunity review* *Accessibility at Citi**.*
*View Citi’s* *EEO Policy Statement* *and the* *Know Your Rights* *poster.*
Salary
170,000\.00 \- 300,000\.00 Annual
Type
Full\-time
Salary Context
This $170K-$300K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $180K across 1616 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 3,057 AI roles we're tracking, AI/ML Engineer positions make up 72% of the market. At Information Technology Senior Management Forum, 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 $179,000 based on 11,905 positions with disclosed compensation. Director-level AI roles across all categories have a median of $243,000. This role's midpoint ($235K) sits 31% above the category median. Disclosed range: $170K to $300K.
Across all AI roles, the market median is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Safety ($274,200). By seniority level: Entry: $97,380; Mid: $160,000; Senior: $227,400; Director: $243,000; VP: $250,000.
Information Technology Senior Management Forum AI Hiring
Information Technology Senior Management Forum has 28 open AI roles right now. They're hiring across Data Scientist, Data Engineer, AI/ML Engineer, AI Software Engineer. Positions span McLean, VA, US, Jersey City, NJ, US, Tampa, FL, US. Compensation range: $126K - $392K.
Location Context
Across all AI roles, 17% (513 positions) offer remote work, while 2,528 require on-site attendance. Top AI hiring metros: New York (2,449 roles, $210,000 median); San Francisco (1,990 roles, $253,000 median); Los Angeles (1,686 roles, $189,000 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 3,057 open positions tracked in our dataset. By seniority: 94 entry-level, 1,467 mid-level, 1,148 senior, and 348 leadership roles (Director, VP, C-Level). Remote roles make up 17% of the market (513 positions). The remaining 2,528 roles require on-site or hybrid attendance.
The market median for AI roles is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($293,500 median, 31 roles); AI Safety ($274,200 median, 51 roles); Research Engineer ($260,000 median, 401 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 3,057 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,189), Data Scientist (233), AI Software Engineer (195). 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 (94) are outnumbered by mid-level (1,467) and senior (1,148) 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 348 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 17% of all AI roles (513 positions), with 2,528 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 $200,000. Top-quartile roles start at $253,000, 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 Engineering Manager roles lead at $293,500 median, while Prompt Engineer roles sit at $142,800. 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 (1,566 postings), Aws (974 postings), Azure (725 postings), Rag (683 postings), Gcp (597 postings), Prompt Engineering (472 postings), Pytorch (461 postings), Claude (447 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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