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About This Role
Posted Date
4/08/2026
Description
We are seeking a results\-driven Generative AI practitioner with end\-to\-end experience for execution and deployment of cutting\-edge Generative AI solutions across our enterprise\-wide Controls Technology platform. In this role, you will be responsible for translating AI strategy into tangible, production\-ready capabilities that enhance operational efficiencies and drive business value. We're looking for someone who combines deep technical expertise in generative AI with a proven track record of successfully delivering complex technology projects.
Key Responsibilities
- GenAI Delivery Leadership: Execute the delivery roadmap for generative AI projects, ensuring alignment with business objectives and timelines. Manage the project lifecycle from ideation and scoping to deployment and post\-launch support.
- Team Leadership \& Mentorship: Build, mentor, and manage a high\-performing team of AI engineers and specialists. Foster a culture of execution, collaboration, and continuous improvement to successfully deliver on the AI roadmap.
- End\-to\-End Solution Delivery: Oversee the design, development, and deployment of robust, scalable, and production\-ready GenAI models. Ensure all solutions meet rigorous performance, security, and quality standards before and after deployment.
- Stakeholder \& Program Management: Serve as the primary point of contact for GenAI delivery. Manage stakeholder expectations, communicate project progress, identify and mitigate risks, and ensure on\-time and on\-budget delivery.
- Cross\-Functional Partnership: Collaborate closely with Data Mesh, Cloud Architecture, MLOps, and business unit teams to ensure the seamless integration and operationalization of AI models into our existing technology ecosystem.
- Technical Excellence \& Best Practices: Drive the adoption of best practices in software development (CI/CD), MLOps, and project management (Agile/Scrum) within the AI team to ensure efficient and repeatable delivery.
- Governance \& Ethical Deployment: Implement and enforce robust governance and ethical AI frameworks throughout the delivery process, ensuring compliance with data privacy standards and corporate policies.
Required Technical Skills
- Large Language Models (LLMs) \& Fine\-Tuning: Deep knowledge of LLMs and advanced fine\-tuning techniques. Proficient in Parameter\-Efficient Fine\-Tuning (PEFT) methods (LoRA, QLoRA, Adapter Tuning, Prefix Tuning), full fine\-tuning, instruction tuning, and agentic AI techniques (RLHF, multi\-task learning).
- Model Optimization: Expertise in model compression and quantization methods (AWQ, GPTQ, GPTQ\-for\-LLaMA). Proficiency with optimized inference engines such as vLLM, DeepSpeed, and FP6\-LLM.
- Prompt Engineering: Adept at advanced prompt engineering techniques and best practices. Familiarity with frameworks that facilitate effective prompt design and management.
- Retrieval\-Augmented Generation (RAG): Advanced knowledge of RAG techniques, including hybrid search, multi\-vector retrieval, Hypothetical Document Embeddings (HyDE), self\-querying, query expansion, re\-ranking, and relevance filtering.
- Machine Learning Frameworks and Cloud Computing: Proficiency in TensorFlow, PyTorch, and Keras. Knowledge of distributed training, parallel processing, and extensive hands\-on experience with AWS services for AI/ML.
- Natural Language Processing (NLP) and AI Deployment: Advanced NLP skills (NER, Dependency Parsing, Text Classification, Topic Modeling). Experience with Transfer Learning, Few\-shot, and Zero\-shot learning. Expertise in containerization (Docker), orchestration (Kubernetes), and CI/CD pipelines for MLOps.
- Data Science, Engineering, and API Development: Strong proficiency in data preprocessing, feature engineering, and handling large\-scale datasets. Experience with real\-time AI applications, streaming data, and designing RESTful APIs for model integration.
- Generative AI Tools \& Platforms: Experienced with LangGraph, Autogen, Crew.ai, LangChain, LlamaIndex, and Hugging Face Transformers. Familiarity with Gen AI APIs (OpenAI, Gemini, Claude) and version control systems like Git.
- AI Compliance \& Guardrails: Knowledge of AI compliance frameworks and best practices. Experience implementing guardrails to ensure ethical AI usage and mitigate risks (e.g., Microsoft's AI Guidance Framework).
Required Leadership \& Soft Skills
- Delivery Leadership: Proven ability to lead and deliver complex, large\-scale technical projects from concept to production.
- Program Management: Expertise in Agile/Scrum methodologies, project planning, resource allocation, and risk management.
- Strategic Execution: Capacity to translate high\-level AI strategy into a concrete, actionable delivery plan and execute it effectively.
- Stakeholder Management: Exceptional ability to manage expectations, communicate complex technical topics clearly, and build strong relationships with both technical and non\-technical stakeholders.
- Pragmatic Innovation: A passion for applying cutting\-edge AI technologies to solve real\-world business problems in a practical and efficient manner.
- Problem Solving: Proactive and analytical mindset to overcome technical and logistical challenges in a fast\-paced environment.
Qualifications
- Overall 14\+ Years experience
- 8\+ years of experience in AI/ML, with at least 3 years in Generative AI.
- 5\+ years of leadership experience managing technical teams and delivering complex software or AI solutions.
- Extensive hands\-on experience with AWS services and infrastructure related to AI/ML.
- A strong portfolio of projects showcasing the successful delivery of AI solutions into a production business environment.
Education
- Bachelor’s or Master’s degree in Computer Science, Data Science, AI, or a related field
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Job Family Group:
Technology
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Job Family:
Architecture
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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:
$156,160\.00 \- $234,240\.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:
Apr 14, 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
156,160\.00 \- 234,240\.00 Annual
Type
Full\-time
Salary Context
This $156K-$234K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $100K across 15465 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 26,159 AI roles we're tracking, AI/ML Engineer positions make up 91% 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 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 $166,983 based on 13,781 positions with disclosed compensation. This role's midpoint ($195K) sits 17% above the category median. Disclosed range: $156K to $234K.
Across all AI roles, the market median is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Architect ($292,900). By seniority level: Entry: $76,880; Mid: $131,300; Senior: $227,400; Director: $244,288; VP: $234,620.
Information Technology Senior Management Forum AI Hiring
Information Technology Senior Management Forum has 44 open AI roles right now. They're hiring across Data Scientist, AI/ML Engineer, AI Engineering Manager, AI Software Engineer. Positions span McLean, VA, US, Irving, TX, US, Fort Worth, TX, US. Compensation range: $100K - $392K.
Location Context
Across all AI roles, 7% (1,863 positions) offer remote work, while 24,200 require on-site attendance. Top AI hiring metros: Los Angeles (1,695 roles, $178,000 median); New York (1,670 roles, $200,000 median); San Francisco (1,059 roles, $244,000 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 26,159 open positions tracked in our dataset. By seniority: 2,416 entry-level, 16,247 mid-level, 5,153 senior, and 2,343 leadership roles (Director, VP, C-Level). Remote roles make up 7% of the market (1,863 positions). The remaining 24,200 roles require on-site or hybrid attendance.
The market median for AI roles is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. Highest-paying categories: AI Engineering Manager ($293,500 median, 28 roles); AI Architect ($292,900 median, 108 roles); AI Safety ($274,200 median, 19 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 26,159 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (23,752), AI Software Engineer (598), AI Product Manager (594). 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 (2,416) are outnumbered by mid-level (16,247) and senior (5,153) 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 2,343 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 7% of all AI roles (1,863 positions), with 24,200 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 $184,000. Top-quartile roles start at $244,000, and the 90th percentile reaches $309,400. 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 $122,200. 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: Rag (16,749 postings), Aws (8,932 postings), Rust (7,660 postings), Python (3,815 postings), Azure (2,678 postings), Gcp (2,247 postings), Prompt Engineering (1,469 postings), Openai (1,269 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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