AI Engineer – Trading Analytics & Data Platforms
CompanyDeloitte
Location
City Of London, England, United Kingdom
Employment TypeFull-time
Posted At4/20/2026
UK Visa Sponsorship Analytics
Analytics are greyed out due to low classification confidence (41.0%).
Occupation TypeIT business analysts, architects and systems designers
Occupation Code Skill LevelHigher Skilled
Sponsorship Salary Threshold
£54,900
(£28.15 per hour)
Occupation rate applies
Above analytics are generated algorithmically based on job titles and may not always be the same as the company's job classification. You can also check detailed occupation eligibility, and salary criteria on our UK Visa Eligible Occupations & Salary Thresholds page.
Disclaimer: Hunt UK Visa Sponsors aggregates job listings from publicly available sources, such as search engines, to assist with your job hunting. We do not claim affiliation with Deloitte. For the most up-to-date job details, please visit the official website by clicking "Apply Now."
Description
AI Engineer – Trading Analytics & Data Platforms
London (Hybrid – 3 days/week onsite)
6 Month Contract Role (Inside IR35 – Competitive Day Rate
Must Have Oil & Gas Industry Experience
About The Role
We are seeking an experienced AI Engineer with a strong data engineering foundation and a passion for solving real-world trading problems. You will work directly with traders and analysts, building AI-powered analytics and data solutions that deliver actionable insights from market pricing and fundamental data—often in near real-time.
This role sits at the intersection of AI, data engineering, and trading, with a focus on rapid prototyping, stakeholder collaboration, and production-grade delivery using modern Azure and Databricks ecosystems.
What You’ll Do
If you’re excited about applying AI and advanced analytics in a trading environment and working directly with front-office stakeholders, we’d love to hear from you.
<\/span>
Design and deliver AI-driven analytics for front-office use, including forecasting, seasonality, correlation, regression, and scenario modelling Build scalable, reusable data pipelines using Databricks (PySpark/Spark, Delta, Unity Catalog), optimizing performance, cost, and reliability Develop real-time and near real-time data solutions to support trading and reporting needs Translate complex trading problems into prototypes and MVPs, iterating rapidly based on feedback Partner closely with traders and analysts to understand requirements and communicate insights effectively Implement LLM and agent-based workflows (prompt engineering, orchestration, retrieval, tool usage, and guardrails) Perform statistical and econometric analysis on large-scale time-series datasets Productionize solutions with robust testing, observability, CI/CD pipelines, and documentation Enable reporting and data access via tools such as Power BI and similar platforms What You’ll Bring Strong hands-on experience with Databricks and Spark (PySpark, SQL, Delta Lake, Unity Catalog) Proven data engineering expertise (data ingestion, modelling, orchestration, performance tuning) Solid foundation in statistics, econometrics, or data science—particularly with market time-series data Experience building AI/ML and LLM-based solutions (prompting, retrieval, agent workflows) Proficiency in Python and modern data/ML tooling (e.g., MLflow, feature stores, vector databases) Famil
London (Hybrid – 3 days/week onsite)
6 Month Contract Role (Inside IR35 – Competitive Day Rate
Must Have Oil & Gas Industry Experience
About The Role
We are seeking an experienced AI Engineer with a strong data engineering foundation and a passion for solving real-world trading problems. You will work directly with traders and analysts, building AI-powered analytics and data solutions that deliver actionable insights from market pricing and fundamental data—often in near real-time.
This role sits at the intersection of AI, data engineering, and trading, with a focus on rapid prototyping, stakeholder collaboration, and production-grade delivery using modern Azure and Databricks ecosystems.
What You’ll Do
If you’re excited about applying AI and advanced analytics in a trading environment and working directly with front-office stakeholders, we’d love to hear from you.
<\/span>
Design and deliver AI-driven analytics for front-office use, including forecasting, seasonality, correlation, regression, and scenario modelling Build scalable, reusable data pipelines using Databricks (PySpark/Spark, Delta, Unity Catalog), optimizing performance, cost, and reliability Develop real-time and near real-time data solutions to support trading and reporting needs Translate complex trading problems into prototypes and MVPs, iterating rapidly based on feedback Partner closely with traders and analysts to understand requirements and communicate insights effectively Implement LLM and agent-based workflows (prompt engineering, orchestration, retrieval, tool usage, and guardrails) Perform statistical and econometric analysis on large-scale time-series datasets Productionize solutions with robust testing, observability, CI/CD pipelines, and documentation Enable reporting and data access via tools such as Power BI and similar platforms What You’ll Bring Strong hands-on experience with Databricks and Spark (PySpark, SQL, Delta Lake, Unity Catalog) Proven data engineering expertise (data ingestion, modelling, orchestration, performance tuning) Solid foundation in statistics, econometrics, or data science—particularly with market time-series data Experience building AI/ML and LLM-based solutions (prompting, retrieval, agent workflows) Proficiency in Python and modern data/ML tooling (e.g., MLflow, feature stores, vector databases) Famil
iarity with CI/CD, Terraform, and production-grade engineering practices Excellent communication and stakeholder management skills, with the ability to work directly with front-office users Nice to Have Experience in commodity or financial trading environments Understanding of market fundamentals, derivatives, P&L, contracts, and trading lifecycle Knowledge of market micro structure, supply-demand dynamics, and risk management concepts Ways of Working Hybrid model with close collaboration alongside trading teams Fast-paced, iterative delivery: prototype quickly, refine with users, and scale to production Strong focus on engineering excellence, including automated testing, governance, and operational reliability Ability to work independently while leveraging support from a broader data and engineering team Who You Are A pragmatic problem-solver who thrives in ambiguous, high-impact environments Comfortable bridging the gap between technical solutions and trading needs Driven to deliver measurable value through AI and data