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Role Summary
The AI Strategy Lead is a hands\-on leadership role responsible for defining and executing the AI strategy across FX trading platforms and engineering functions. This role combines strategic ownership with direct technical involvement in building and scaling AI\-driven solutions.
Reporting to the Director of FX Exchange Engineering, this individual will work closely with engineering leads, product, and architecture teams to translate AI opportunities into production\-grade solutions, with a strong focus on standardization, platform reuse, and measurable impact.
Key Responsibilities
1\. Strategy with Direct Execution
- Define and drive the AI strategy aligned to FX platform and engineering priorities.
- Personally contribute to the design and implementation of AI/ML solutions (e.g., prototypes, frameworks, integrations).
- Lead by example in moving from PoCs to production\-grade systems.
- Identify and prioritize high\-impact use cases such as:
+ AI\-driven test automation
+ Intelligent workflow automation
+ Developer productivity (code generation, CI/CD optimization)
2\. Hands\-On Engineering Leadership
- Work directly with teams on architecture, design, and implementation of AI solutions.
- Contribute to code, frameworks, or reusable components where needed.
- Establish engineering best practices for:
+ Model integration into Java\-based services
+ API\-driven AI services
+ Messaging\-based integration (e.g., event\-driven systems)
- Drive adoption of common libraries, shared frameworks, and reusable services.
3\. Platform \& Standardization Focus
- Define and implement a shared AI platform approach aligned with enterprise architecture:
+ Common APIs and services for AI capabilities
+ Reusable components across FX venues
+ Integration with existing platform constructs (e.g., messaging bus, FIX, shared services)
- Avoid one\-off solutions by enforcing platform\-first design and reuse.
4\. Cross\-Team Delivery Ownership
- Partner with global engineering teams (NY, London, Hyderabad, Bangkok) to deliver AI\-enabled capabilities.
- Ensure clear ownership, execution tracking, and delivery accountability.
- Actively unblock teams and resolve technical or execution challenges.
5\. MLOps \& Productionization
- Drive best practices for deploying and managing AI solutions in production:
+ Model lifecycle management
+ Monitoring, observability, and performance tuning
+ CI/CD integration for AI components
- Ensure solutions are scalable, secure, and compliant with enterprise requirements.
6\. Governance \& Responsible AI
- Implement practical governance for AI usage (data quality, explainability, auditability).
- Ensure compliance with regulatory expectations for financial systems.
- Define guardrails for GenAI adoption (security, data leakage, model usage).
7\. Innovation with Practical Outcomes
- Evaluate emerging technologies (GenAI, LLMs, AI agents) with a focus on real engineering impact .
- Lead targeted PoCs—but with a clear path to production or discard.
- Introduce tooling and frameworks that improve engineering productivity.
8\. Capability Building (Player\-Coach Model)
- Mentor engineers and leads on AI/ML concepts and implementation patterns.
- Upskill existing teams rather than relying solely on specialist roles.
- Influence hiring strategy for targeted AI/ML skills where needed.
Required Qualifications
- Bachelor’s or Master’s degree in Computer Science, Engineering, or related field.
- 10\+ years of software engineering experience, with hands\-on development background .
- Demonstrated experience delivering AI/ML or data\-driven solutions in production.
- Strong programming experience (preferably Java or similar enterprise stack).
- Solid understanding of:
+ Distributed systems and APIs
+ Event\-driven / messaging architectures
+ Integration of AI into production systems
Preferred Qualifications
- Experience in financial markets / trading systems (FX ideally).
- Exposure to GenAI / LLM integration in enterprise environments.
- Experience with MLOps or AI platform engineering.
- Familiarity with developer tooling automation and test automation frameworks.
Key Skills \& Competencies
- Strong hands\-on engineering capability
- Ability to balance strategy with execution
- Platform mindset (reuse, standardization, scalability)
- Clear ownership and delivery focus
- Strong cross\-team collaboration and influence
- Pragmatic decision\-making (avoid over\-engineering / unnecessary customization)
Career Stage:
Manager
Compensation/Benefits Information:
LSEG is committed to offering competitive Compensation and Benefits. The anticipated base salary for this position is $147,500 \- $245,900\.
Please be aware base salary ranges may vary by geographic location, city and state. In addition to our offered base salary, this role is eligible for our Annual Incentive Plan (AIP/”bonus plan”). Target AIP rates will be commensurate with role level and posted career stage. Individual salary will be reflective of job related knowledge, skills and equivalent experience. LSEG roles (excluding internships and part\-time roles of less than 20 hours per week) are typically eligible for inclusion in our LSEG Benefits program, which includes offerings of: Annual Wellness Allowance, Paid time\-off, Medical, Dental, Vision, Flex Spending \& Health Savings Options, Prescription Drug plan, 401(K) Savings Plan and Company match. LSEG’s Benefits plan also includes basic life insurance, disability benefits, emergency backup dependent care, adoption assistance commuter assistance etc.
London Stock Exchange Group (LSEG) Information:
Join us and be part of a team that values innovation, quality, and continuous improvement. If you're ready to take your career to the next level and make a significant impact, we'd love to hear from you.
LSEG is a leading global financial markets infrastructure and data provider. Our purpose is driving financial stability, empowering economies and enabling customers to create sustainable growth.
Our purpose is the foundation on which our culture is built. Our values of Integrity, Partnership , Excellence and Change underpin our purpose and set the standard for everything we do, every day. They go to the heart of who we are and guide our decision making and everyday actions.
Working with us means that you will be part of a dynamic organisation of 25,000 people across 65 countries. However, we will value your individuality and enable you to bring your true self to work so you can help enrich our diverse workforce.
We are proud to be an equal opportunities employer. This means that we do not discriminate on the basis of anyone’s race, religion, colour, national origin, gender, sexual orientation, gender identity, gender expression, age, marital status, veteran status, pregnancy or disability, or any other basis protected under applicable law. Conforming with applicable law, we can reasonably accommodate applicants' and employees' religious practices and beliefs, as well as mental health or physical disability needs.
You will be part of a collaborative and creative culture where we encourage new ideas. We are committed to sustainability across our global business and we are proud to partner with our customers to help them meet their sustainability objectives. Our charity, the LSEG Foundation provides charitable grants to community groups that help people access economic opportunities and build a secure future with financial independence. Colleagues can get involved through fundraising and volunteering.
LSEG offers a range of tailored benefits and support, including healthcare, retirement planning, paid volunteering days and wellbeing initiatives.
Please take a moment to read this privacy notice carefully, as it describes what personal information London Stock Exchange Group (LSEG) (we) may hold about you, what it’s used for, and how it’s obtained, your rights and how to contact us as a data subject .
If you are submitting as a Recruitment Agency Partner, it is essential and your responsibility to ensure that candidates applying to LSEG are aware of this privacy notice.
Salary Context
This $147K-$245K range is above the median for AI/ML Engineer roles in our dataset (median: $180K across 1841 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 3,708 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At LSEG (London Stock Exchange Group), 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 $218,750 based on 3,817 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $230,000. This role's midpoint ($196K) sits 10% below the category median. Disclosed range: $147K to $245K.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
LSEG (London Stock Exchange Group) AI Hiring
LSEG (London Stock Exchange Group) has 2 open AI roles right now. They're hiring across AI/ML Engineer, AI Product Manager. Positions span Liberty, NY, US, New York, NY, US. Compensation range: $245K - $245K.
Location Context
Across all AI roles, 14% (508 positions) offer remote work, while 3,180 require on-site attendance. Top AI hiring metros: New York (1,045 roles, $220,000 median); San Francisco (810 roles, $277,088 median); Los Angeles (397 roles, $215,000 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 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 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 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). 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 (102) are outnumbered by mid-level (1,705) and senior (1,469) 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 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 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 $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. 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 $300,000 median, while Prompt Engineer roles sit at $140,000. 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 (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 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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