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About This Role
Posted Date
6/01/2026
Description
Our Vision
We are building a next\-generation system that reimagines banking workflows for our Corporate, Commercial, and Investment Bankers. Our vision is to empower them with a revolutionary Agentic AI Platform, featuring intelligent, autonomous agents that streamline processes, uncover new opportunities, and deepen client relationships—ultimately leading to significant productivity gains and increased wallet share. We are looking for a visionary, hands\-on engineering leader to build and scale the platform that will make this a reality.
The Role
As the Director of Agentic Platform Engineering, you will be a player\-coach responsible for the technical vision, architecture, and execution of this greenfield platform. You will lead a world\-class engineering team from the ground up, while remaining deeply technical and contributing to the core development of the platform. This is a unique opportunity to blend strategic leadership with hands\-on engineering to build a product that will have a direct and measurable impact on the front lines of our business.
Key Responsibilities
- Platform Architecture \& Development: Lead the design, architecture, and hands\-on development of a scalable, secure, and resilient agentic AI platform from concept to production.
- Technical Leadership \& Hands\-On Engineering: Serve as the lead engineer and technical authority, guiding critical decisions on frameworks, technologies, and infrastructure. You will be expected to write code, build prototypes, and lead by example.
- Team Building \& Mentorship: Recruit, hire, and mentor a high\-performing, agile team of software and machine learning engineers. Foster a culture of innovation, excellence, and accountability.
- Strategic Roadmapping: Partner closely with product management and senior business leaders in banking to define the product strategy and technical roadmap. Translate complex business needs into elegant technical solutions.
- Cross\-Functional Collaboration: Partner effectively with horizontal AI platform teams, enterprise architecture, and external vendor partners to leverage existing capabilities, influence roadmaps, and accelerate delivery.
- AI \& ML Integration: Drive the strategy for integrating and operationalizing Large Language Models (LLMs), agentic frameworks (e.g., Google ADK, LangChain,), and other AI/ML technologies to solve real\-world banking challenges.
- Operational Excellence: Implement and champion best\-in\-class engineering practices, including CI/CD, automated testing, infrastructure\-as\-code, and robust monitoring to ensure enterprise\-grade reliability.
- Business Impact: Define, measure, and report on key performance indicators (KPIs) related to platform adoption, user productivity, and the ultimate impact on business outcomes like wallet share growth.
- Compliance \& Security: Ensure the platform adheres to the highest standards of data privacy, security, and regulatory compliance required in the banking industry.
Qualifications \& Experience
- Education: Bachelor's or Master’s degree in Computer Science, Engineering, or a related technical field.
- Experience: 12\+ years of experience in software engineering, with at least 4\+ years in a leadership role, leading high\-performing engineering teams.
- Hands\-On Leader: Proven experience as a "player\-coach" who can lead from the front, contribute to the codebase, and mentor junior and senior engineers.
- Platform Building: A strong track record of designing, building, and launching scalable, distributed, cloud\-native platforms from the ground up.
- Domain Knowledge: Experience in the financial services industry (Corporate Banking, Investment Banking, FinTech) is a significant plus. An understanding of banking workflows and data is highly desirable.
- Communication Skills: Exceptional ability to communicate complex technical concepts to non\-technical stakeholders and to articulate a clear technical vision that aligns with business goals.
Technical Skills
- AI / GenAI / Agentic Platforms
+ LLM \& Agentic Frameworks: Deep expertise in building production\-grade agentic systems using GCP as primary (ADK, Vertex AI)
+ Multi\-Agent Systems: Hands\-on experience designing and implementing multi\-agent architectures(task decomposition, coordination, orchestration, and agent\-to\-agent (A2A) interaction patterns)
+ Model Context Protocol (MCP) \& Integrations: Experience integrating agents with enterprise tools and data sources using MCP or equivalent context\-sharing patterns
+ Knowledge Graphs \& Reasoning: Building and leveraging knowledge graphs for context enrichment, reasoning, and workflow automation
+ RAG \& Knowledge Systems: End\-to\-end RAG pipelines using enterprise search \+ vector stores (e.g., Elastic, Pinecone) with grounding, evaluation, and optimization
+ Model Lifecycle \& Governance: Model evaluation, monitoring, prompt/version control, and Responsible AI / MRM compliance
- Enterprise AI Integration \& Data
+ API\- and event\-driven integration of AI into enterprise workflows
+ Data platforms: Databricks, Spark, Snowflake; streaming via Kafka
- Backend \& Distributed Systems
+ Languages: Python (expert), Java/Spring Boot (enterprise standard), Go (plus)
+ Architecture: Microservices, domain\-driven design, event\-driven systems
+ APIs \& Integration: REST/gRPC; Apigee, Kong
+ Data \& Messaging: PostgreSQL/Oracle, MongoDB/Cassandra, Kafka
- Cloud \& DevSecOps
+ Cloud Platforms: Strong experience with GCP (preferred); working knowledge of AWS; Azure exposure optional (not a dependency)
+ Containers: Docker, Kubernetes (GKE/EKS)
+ IaC: Terraform
+ CI/CD: GitHub Actions, Jenkins
+ Observability: Splunk, ELK, Prometheus, Grafana
- Security \& Compliance
+ Secure coding, API security, Zero Trust
+ Data privacy, encryption, access control
+ Regulatory compliance and AI governance (MRM)
What We Offer
- A leadership role in a high\-priority, transformative initiative with executive visibility.
- The opportunity to build a cutting\-edge AI platform from scratch.
- A competitive compensation package including salary, bonus, and benefits.
- A collaborative and innovative culture that values technical excellence and business impact.
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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:
New York New York 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 08, 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: $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. 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 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
AI roles in New York pay a median of $210,000 across 2,448 tracked positions. That's 5% above the national 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
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