Interested in this AI/ML Engineer role at TP ICAP?
Apply Now →Skills & Technologies
About This Role
Group Overview
The TP ICAP Group is a world leading provider of market infrastructure.
Our purpose is to provide clients with access to global financial and commodities markets, improving price discovery, liquidity, and distribution of data, through responsible and innovative solutions.
Through our people and technology, we connect clients to superior liquidity and data solutions.
The Group is home to a stable of premium brands. Collectively, TP ICAP is the largest interdealer broker in the world by revenue, the number one Energy & Commodities broker in the world, the world’s leading provider of OTC data, and an award winning all-to-all trading platform.
Founded in London in 1866, the Group operates from more than 60 offices in 27 countries. We are 5,200 people strong. We work as one to achieve our vision of being the world’s most trusted, innovative, liquidity and data solutions specialist.
Role Overview
This is a newly created opportunity in our Global Broking division for an AI Engineer.
TP ICAP is a leading global provider of market infrastructure and data solutions, specializing in financial services. With a strong commitment to innovation, we help clients navigate the complexities of global markets by offering advanced technology solutions that enhance decision-making and drive growth. As we continue to expand our technological capabilities, we have established a dedicated AI & Innovation Lab to develop and deliver transformative AI-based solutions across the organization. Join us in shaping our future through cutting-edge artificial intelligence technology.
Design, develop and maintain advanced AI services, working as a dedicated spoke of the central AI & Innovation Lab. Collaborate with stakeholders, the central AI team, and business partners to deliver impactful AI-driven solutions that optimize internal processes. While reporting administratively to the Regional COO, you will maintain a strong dotted-line relationship with the AI & Innovation Lab, actively participating in lab initiatives, cross-spoke collaboration, and the continuous evolution of TP ICAP's AI capabilities. A focus on technical excellence to ensure AI is delivered in a robust, scalable manner and that technical best practices are maintained.
Role Responsibilities
- Architect and design innovative AI solutions tailored to business challenges
- Build and deploy cutting-edge AI applications for use cases, ensuring that solutions are high-performing, scalable, and optimized for real-time operations
- Translate stakeholder requirements into technical solutions, ensuring alignment with both business objectives and the technical standards established by the central AI & Innovation Lab
- Scale our AI agent solutions while maintaining strict data stewardship and compliance standards
- Co-develop and enhance the shared AI service platform and frameworks with the AI & Innovation Lab, contributing insights and requirements while customizing solutions for domain-specific requirements
- Provide ongoing support and maintenance for deployed AI solutions, ensuring smooth operation and quick resolution of any issues affecting processes
- Present AI project updates and key developments to relevant leadership and business stakeholders, demonstrating value and ensuring alignment with AI strategy
Experience / Competences
Essential
- Expertise in AI technologies, including Generative AI and Machine Learning
- Hands-on experience working with Large Language Models (LLMs)
- Experience with Python in AI/ML frameworks and libraries
- Track record in the architecture and design of domain-specific solutions with expertise in building scalable and robust services in production environments
- Experience in managing tech priorities within an Agile backlog, resolving dependencies, and ensuring alignment with both business objectives and central AI team standards
- Evidence of presenting complex technical concepts to internal professionals and business stakeholders with varying technical backgrounds
Desired
- AWS experience, including AI services such as Bedrock and the Serverless ecosystem, with the ability to work within established cloud frameworks
- Proficiency in front-end technologies, particularly with React. Evidence of building intuitive, responsive user interfaces that integrate seamlessly with AI-driven back-end services
- Expertise in back-end development, with experience in building and deploying scalable APIs and microservices to support AI solutions
- Knowledge of OAuth (Okta) for implementing secure, scalable authentication and authorization mechanisms in full-stack applications, ensuring data protection and privacy across AI solutions
- We are looking for a proactive, adaptable, and detail-oriented individual who thrives in fast-paced environments and embraces challenges.
- The ideal candidate will be open to innovative ideas, with a strong analytical mindset and problem-solving skills.
- They will demonstrate excellent communication, tailoring their style to suit different audiences, and engage confidently while respecting professional and company values.
- With a keen eye for detail, they will stay organized, remain calm under pressure, and consistently seek opportunities for improvement while maintaining strong collaboration with the central AI & Innovation Lab
$120,000-$150,000
Band & level: Professional, 4
Not The Perfect Fit?
Concerned that you may not meet the criteria precisely? At TP ICAP, we wholeheartedly believe in fostering inclusivity and cultivating a work environment where everyone can flourish, regardless of your personal or professional background. If you are enthusiastic about this role but find that your experience doesn't align perfectly with every aspect of the job description, we strongly encourage you to apply. You may be the ideal candidate for this position or another opportunity within our organisation. Our dedicated Talent Acquisition team is here to assist you in recognising how your unique skills and abilities can be a valuable contribution. Don't hesitate to take the leap and explore the possibilities. Your potential is what truly matters to us.
Company Statement
We know that the best innovation happens when diverse people with different perspectives and skills work together in an inclusive atmosphere. That's why we're building a culture where everyone plays a part in making people feel welcome, ready and willing to contribute. TP ICAP Accord - our Employee Network - is a central to this. As well as representing specific groups, TP ICAP Accord helps increase awareness, collaboration, shares best practice, and holds our firm to account for driving continuous cultural improvement.
Location
USA - 200 Vesey Street - New York, NY
Salary Context
This $120K-$150K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $175K across 1356 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 8,943 AI roles we're tracking, AI/ML Engineer positions make up 75% of the market. At TP ICAP, 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 $212,000 based on 11,866 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($135K) sits 36% below the category median. Disclosed range: $120K to $150K.
Across all AI roles, the market median is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,250; VP: $235,000.
TP ICAP AI Hiring
TP ICAP has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in New York, NY, US. Compensation range: $150K - $150K.
Location Context
AI roles in New York pay a median of $221,000 across 2,878 tracked positions. That's 4% 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 8,943 open positions tracked in our dataset. By seniority: 175 entry-level, 4,132 mid-level, 3,268 senior, and 1,368 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (521 positions). The remaining 8,351 roles require on-site or hybrid attendance.
The market median for AI roles is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 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 8,943 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,739), AI Product Manager (403), AI Software Engineer (402). 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 (175) are outnumbered by mid-level (4,132) and senior (3,268) 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 1,368 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (521 positions), with 8,351 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 $213,380. Top-quartile roles start at $264,148, and the 90th percentile reaches $319,310. 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 $253,100 median, while Prompt Engineer roles sit at $141,440. 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 (1,422 postings), Aws (808 postings), Azure (638 postings), Rag (555 postings), Gcp (457 postings), Pytorch (433 postings), Claude (341 postings), Kubernetes (337 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.