Interested in this AI/ML Engineer role at Barclays?
Apply Now →Skills & Technologies
About This Role
Date live: 07/13/2026
Business Area: Risk Finance and Treasury
Area of Expertise: Technology
Contract: Permanent
Reference Code: JR\-0000122146
Salary / Rate $ 120,000\.00 \- $ 175,000\.00
Embark on a transformative journey as a Senior GenAI and Python Developer – AVP. At Barclays, our vision is clear –to redefine the future of banking and help craft innovative solutions. In this role, you will leverage your deep AWS expertise to help a large cross\-functional team deliver multiple Generative AI solutions each year. These programs will have a direct impact on both end users and the business by reducing manual effort, streamlining workflows, and accelerating key processes. You will play a critical role in developing transformative AI technologies while gaining practical experience with some of the latest large language models available through AWS Bedrock.
To be successful as a Senior GenAI and Python Developer – AVP, you should have:
- Practical development experience with Python 3\.12\+ and ample proficiency in LangChain, machine learning frameworks, the Amazon Bedrock SDK, and related AI/ML technologies
- Extensive AWS experience, including services such as Amazon EKS, Amazon Bedrock, Knowledge Bases, CloudWatch, AgentCore, Strands SDK, Auto Scaling Groups, Load Balancers, API Gateway, Lambda, Step Functions, AWS Glue, PostgreSQL, and Amazon RD
- Considerable experience developing and deploying FastAPI\-based applications, with experience in frontend/client integration and API consumption patterns
- Experience with observability and FinOps tools, such as Langfuse and Phoenix, to monitor, evaluate, and optimize GenAI application performance and costs
- Experience designing and building Agentic RAG (Retrieval\-Augmented Generation) solutions and multi\-agent AI systems using industry\-standard agentic AI frameworks and best practices
Other highly valued skills include:
- Experience working with Large Language Models (LLMs) on Amazon Bedrock, including application development, model integration, and orchestration using AWS SDKs and related services
- Proven experience building and maintaining CI/CD pipelines to support automated testing, continuous integration, and deployment of applications to Kubernetes\-based environments
- Ample understanding of AI guardrails, governance, and security best practices, particularly when working with sensitive financial data and regulated datasets
- Familiarity with GitLab and GitLab CI/CD for source control management, automated builds, and deployment workflows
- Working knowledge of Helm charts, AWS CloudFormation, and infrastructure\-as\-code practices to enable scalable, repeatable, and seamless deployments to Amazon EKS environments
You may be assessed on the key critical skills relevant for success in this role, such as risk and controls, change and transformation, business acumen, strategic thinking, digital and technology, as well as job\-specific technical skills.
This role is located in our Whippany, NJ office.
Minimum Salary: $120,000
Maximum Salary: $175,000
The minimum and maximum salary/rate information above includes only base salary or base hourly rate. It does not include any other type of compensation or benefits that may be available.
Barclays employees are eligible for a suite of competitive and generous employee benefits, including medical, dental and vision coverage, 401(k), life insurance, and other paid leave for qualifying circumstances.
This position is eligible for an incentive award.
Purpose of the role
To design, develop and improve software, utilising various engineering methodologies, that provides business, platform, and technology capabilities for our customers and colleagues.
Accountabilities
- Development and delivery of high\-quality software solutions by using industry aligned programming languages, frameworks, and tools. Ensuring that code is scalable, maintainable, and optimized for performance.
- Cross\-functional collaboration with product managers, designers, and other engineers to define software requirements, devise solution strategies, and ensure seamless integration and alignment with business objectives.
- Collaboration with peers, participate in code reviews, and promote a culture of code quality and knowledge sharing.
- Stay informed of industry technology trends and innovations and actively contribute to the organization’s technology communities to foster a culture of technical excellence and growth.
- Adherence to secure coding practices to mitigate vulnerabilities, protect sensitive data, and ensure secure software solutions.
- Implementation of effective unit testing practices to ensure proper code design, readability, and reliability.
Assistant Vice President Expectations
- To advise and influence decision making, contribute to policy development and take responsibility for operational effectiveness. Collaborate closely with other functions/ business divisions.
- Lead a team performing complex tasks, using well developed professional knowledge and skills to deliver on work that impacts the whole business function. Set objectives and coach employees in pursuit of those objectives, appraisal of performance relative to objectives and determination of reward outcomes
- If the position has leadership responsibilities, People Leaders are expected to demonstrate a clear set of leadership behaviours to create an environment for colleagues to thrive and deliver to a consistently excellent standard. The four LEAD behaviours are: L – Listen and be authentic, E – Energise and inspire, A – Align across the enterprise, D – Develop others.
- OR for an individual contributor, they will lead collaborative assignments and guide team members through structured assignments, identify the need for the inclusion of other areas of specialisation to complete assignments. They will identify new directions for assignments and/ or projects, identifying a combination of cross functional methodologies or practices to meet required outcomes.
- Consult on complex issues; providing advice to People Leaders to support the resolution of escalated issues.
- Identify ways to mitigate risk and developing new policies/procedures in support of the control and governance agenda.
- Take ownership for managing risk and strengthening controls in relation to the work done.
- Perform work that is closely related to that of other areas, which requires understanding of how areas coordinate and contribute to the achievement of the objectives of the organisation sub\-function.
- Collaborate with other areas of work, for business aligned support areas to keep up to speed with business activity and the business strategy.
- Engage in complex analysis of data from multiple sources of information, internal and external sources such as procedures and practises (in other areas, teams, companies, etc).to solve problems creatively and effectively.
- Communicate complex information. 'Complex' information could include sensitive information or information that is difficult to communicate because of its content or its audience.
- Influence or convince stakeholders to achieve outcomes.
All colleagues will be expected to demonstrate the Barclays Values of Respect, Integrity, Service, Excellence and Stewardship – our moral compass, helping us do what we believe is right. They will also be expected to demonstrate the Barclays Mindset – to Empower, Challenge and Drive – the operating manual for how we behave.
Salary Context
This $120K-$175K range is below 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 Barclays, 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 $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 ($147K) sits 33% below the category median. Disclosed range: $120K to $175K.
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.
Barclays AI Hiring
Barclays has 5 open AI roles right now. They're hiring across AI/ML Engineer, Data Scientist. Positions span Whippany, NJ, US, New York, NY, US. Compensation range: $175K - $230K.
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
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.