Interested in this AI/ML Engineer role at Information Technology Senior Management Forum?
Apply Now →Skills & Technologies
About This Role
Posted Date
6/04/2026
Description
We are seeking an experienced Senior Generative AI Developer to help drive the design, development, and integration of state\-of\-the\-art Generative AI solutions across our enterprise Controls Technology platform. You will collaborate with cross\-functional teams, contribute deep technical expertise, and play a key role in delivering scalable AI solutions to enhance automation and operational efficiency.
Key Responsibilities:
- Collaborate with AI architects, leads, and stakeholders to design and implement generative AI solutions that address business challenges.
- Develop, fine\-tune, and optimize large language models (LLMs), leveraging both parameter\-efficient techniques and full fine\-tuning where applicable.
- Implement and experiment with advanced generative AI methods, including prompt engineering and Retrieval\-Augmented Generation (RAG).
- Support the integration of AI models into production environments, ensuring robust deployment, scalability, and maintainability.
- Contribute to the development and optimization of real\-time and streaming AI solutions.
- Stay current with the latest advances in generative AI and actively share knowledge with the team.
- Ensure adherence to ethical AI guidelines, data privacy, and compliance standards.
- Mentor junior team members, provide code reviews, and foster a culture of technical excellence.
Required Technical Skills:
- Strong hands\-on experience with LLMs and fine\-tuning methods such as LoRA, QLoRA, Adapter/Prefix Tuning, and instruction tuning.
- Practical knowledge of model optimization (compression, quantization) and familiarity with tools such as DeepSpeed, vLLM, GPTQ, or similar.
- Proficient in prompt engineering and familiarity with prompt design tools/frameworks.
- Experience building RAG systems, including hybrid search and multi\-vector retrieval.
- Proficient with machine learning frameworks (PyTorch, TensorFlow, Keras) and distributed training.
- Strong skills in NLP (NER, Dependency Parsing, Text Classification, Topic Modeling), transfer learning, and advanced learning paradigms.
- Experience with containerization (Docker), orchestration (Kubernetes), and CI/CD pipelines for ML models.
- Familiarity with generative AI tools like LangChain, LlamaIndex, Hugging Face, and major GenAI APIs (OpenAI, Gemini, Claude, etc.).
- Solid understanding of AI compliance, guardrails, and responsible AI practices.
- Strong skills in Python and experience with data preprocessing, feature engineering, and API development.
Required Soft Skills:
- Strong collaboration skills to work effectively in cross\-functional teams.
- Analytical and proactive approach to problem\-solving.
- Clear communication skills for both technical and non\-technical audiences.
- Eagerness to learn, innovate, and mentor less experienced developers.
Qualifications:
- At least 5 years of experience in AI/ML, including significant experience in Generative AI.
- Demonstrated portfolio of successful AI\-driven projects in a business environment.
- Experience working with AWS (or equivalent) cloud infrastructure for AI/ML.
Education:
- Bachelor’s or master’s degree in Computer Science, Data Science, AI, or a related field.
\-
#### Job Family Group:
Technology
\-
#### Job Family:
Applications Development
\-
#### Time Type:
Full time
\-
#### Primary Location:
Tampa Florida United States
\-
#### Primary Location Full Time Salary Range:
$113,840\.00 \- $170,760\.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.
\-
#### Most Relevant Skills
Please see the requirements listed above.
\-
#### Other Relevant Skills
For complementary skills, please see above and/or contact the recruiter.
\-
#### Anticipated Posting Close Date:
Jun 09, 2026
\-
*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
113,840\.00 \- 170,760\.00 Annual
Type
Full\-time
Salary Context
This $113K-$170K range is below the median for AI/ML Engineer roles in our dataset (median: $181K across 1996 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 3,824 AI roles we're tracking, AI/ML Engineer positions make up 71% 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 $178,940 based on 11,900 positions with disclosed compensation. This role's midpoint ($142K) sits 20% below the category median. Disclosed range: $113K to $170K.
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 33 open AI roles right now. They're hiring across Data Scientist, Data Engineer, AI Software Engineer, AI/ML Engineer. Positions span McLean, VA, US, Jersey City, NJ, US, Irving, TX, US. Compensation range: $126K - $392K.
Location Context
Across all AI roles, 16% (613 positions) offer remote work, while 3,187 require on-site attendance. Top AI hiring metros: New York (2,448 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 3,824 open positions tracked in our dataset. By seniority: 119 entry-level, 1,813 mid-level, 1,472 senior, and 420 leadership roles (Director, VP, C-Level). Remote roles make up 16% of the market (613 positions). The remaining 3,187 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 3,824 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,702), Data Scientist (281), AI Software Engineer (258). 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 (119) are outnumbered by mid-level (1,813) and senior (1,472) 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 420 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 16% of all AI roles (613 positions), with 3,187 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 (1,968 postings), Aws (1,203 postings), Azure (882 postings), Rag (877 postings), Gcp (735 postings), Prompt Engineering (587 postings), Pytorch (586 postings), Claude (554 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
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.