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About the Role

Lead a high-impact engineering group responsible for building, scaling, and operationalizing data and AI capabilities across Engelhart’s trading, quantitative, risk, and operational functions. This is a hands-on leadership role at the intersection of data engineering, AI platform delivery, front-office technology, and commodities trading. The role reports to the Global Head of Tech - AI, Data & UX and will play a central part in implementing Engelhart’s AI Business Plan, positioning AI as a federated capability enabled by a central Tech AI team.

Responsibilities

Lead and develop the AI & Data engineering team, setting clear priorities, coaching technical growth, reviewing delivery quality, and ensuring the team operates with high ownership, collaboration, and continuous improvement.
Drive implementation of the Agentic AI platform, supporting orchestration layers, agent runtimes, AWS-hosted services, Lambda-based tooling, MCP servers, knowledge bases, vector databases, retrieval pipelines, agent skills and secure deployment patterns.
Support implementation of the AI Business Plan, helping deliver the central Tech AI team’s mandate across platform, governance and delivery.
Support delivery of AI use cases such as Trader Copilot, Quant Copilot, Risk Copilot, Treasury Copilot, Power Forecast Copilot, reconciliation automation, AI Explorer, text-to-SQL, Confluence/wiki agents, contract/vendor agents and self-service AI workflows.
Own delivery of scalable data engineering capabilities across Engelhart’s data landscape, including ingestion, cleansing, enrichment, transformation, storage, distribution, visualisation and operational reuse.
Partner with Quant and Systematic teams to enable data, AI and engineering capabilities that support research workflows, model-adjacent analytics, signal review, back testing support, code review, documentation and systematic trading productivity.
Translate front-office requirements into practical technology solutions, working closely with Traders, Quants, FO Analysts, Risk and other business teams.
Champion production-grade engineering standards, including robust architecture, secure data access, maintainable code, CI/CD, infrastructure-as-code, observability, documentation, testing and clear operational ownership.
Ensure AI solutions are governed appropriately, contributing to standards around evaluation, provenance, human-in-the-loop design, auditability, monitoring, access control and safe deployment.
Measure and communicate value delivered, helping connect technical delivery to adoption, cost savings, front-office efficiency and business impact.

Requirements

Experienced engineering manager from a commodities trading environment.
Comfortable leading a team of senior engineers while remaining close enough to the architecture and code to challenge design decisions, unblock delivery and set a high technical bar.
Understand that AI adoption depends on data quality, platform reliability, governance, user trust and day-to-day workflow fit.
Strong domain understanding of commodities trading is essential.
Academic background or equivalent professional experience in Computer Science, Engineering, Data Science, Information Systems, Quantitative Finance, Mathematics or a related technical field.
Significant experience leading data engineering, AI engineering, platform engineering or technology delivery teams in a demanding business environment.
Proven experience managing, coaching and developing engineers; including senior individual contributors and early-career technical talent.
Strong hands-on understanding of modern data engineering practices, including data ingestion, transformation, cleansing, storage, distribution, data quality, documentation and operational support.
Advanced proficiency in Python and the wider Python data ecosystem, including experience with Pandas or comparable libraries.
Experience with analytical, time-series, or large-scale data platforms (ClickHouse preferred, Redshift relevant).
Strong experience with cloud-native engineering, preferably AWS, including practical understanding of services such as S3, Lambda, Athena, EMR, Fargate, Kinesis, EC2, API Gateway, CloudWatch, and related architecture and security patterns.
Hands-on experience with Docker, Git, CI/CD, infrastructure-as-code, and modern software delivery practices.
Practical understanding of AI, LLM, or Generative AI application architecture, including RAG, knowledge bases, vector databases, prompt engineering, evaluation, orchestration, agent workflows, or AI-assisted developer tooling.
Experience engaging directly with Traders, Quant developers, Data Scientists, analysts or front-office stakeholders to gather requirements, challenge assumptions and deliver useful technical solutions.
Strong understanding of commodities trading, particularly Oil, Power, and Gas; including the role of market data, analytics, risk, weather, fundamentals, trading workflows and front-office decision support.
Ability to translate complex technical concepts for both technical and non-technical audiences, influencing decisions and building trust with stakeholders across levels.
Strong organisational skills and a strong sense of ownership, accountability, and follow-through.
Bias toward delivering production-grade solutions rather than prototypes that do not land.

Skills

Python
Pandas
ClickHouse
Redshift
AWS
S3
Lambda
Athena
EMR
Fargate
Kinesis
EC2
API Gateway
CloudWatch
Docker
Git
CI/CD
Infrastructure-as-code
AI
LLM
Generative AI
RAG
Knowledge bases
Vector databases
Prompt engineering
Orchestration
Agent workflows
AI-assisted developer tooling
Commodities trading
Oil
Power
Gas
Market data
Analytics
Risk
Weather
Fundamentals
Trading workflows
Front-office decision support
Systematic trading
Quantitative research platforms
Trading analytics
Signal generation
Back testing workflows
Model-adjacent engineering
Agentic AI frameworks
LangChain
n8n
MCP servers
AWS Bedrock
Postgres with pgvector
Bedrock Knowledge Bases
Pinecone
Chroma
Private model deployment
VPC-based architectures
Data-access controls
Auditability
Monitoring
Vendor due diligence
AI governance
Evaluation suites
Provenance
Human-in-the-loop workflows
Risk classification
Model monitoring
Audit trails
EU AI Act
REST APIs
SSIS
Entity Framework
Internal API design
Python packages
Integration patterns
Apache Spark
Databricks
Parquet
Hive
Plotly
GitHub Copilot
Internal AI chat
Summarization pipelines
AI productivity tooling
Federated technology model
External data vendors
Cloud providers
Consulting partners
Group-level technology partners

Location

London

Work Type

Full-time
Onsite

Experience Level

Senior

Education Level

Computer Science
Engineering
Data Science
Information Systems
Quantitative Finance
Mathematics

Benefits

Competitive compensation
Participation in Engelhart’s discretionary bonus plan
25 days of annual holiday entitlement, excluding UK public holidays
Robust benefits package such as medical, dental, life insurance
Generous pension contribution
Supplemental benefits partially subsidised by the Company
Eligibility to receive external and internal training

About the Company

Engelhart was founded in 2013 by BTG Pactual Group as a commodities trading company.
Our business model is “asset light” and highly diversified – giving us the ability to adapt effectively and nimbly to changing market conditions.
We have assembled successful multidisciplinary teams, leveraging advanced fundamental analysis with deep quantitative and weather research capabilities.
Our activities are underpinned by strong risk management practices and by powerful technology and operational excellence.
We have exceptional teams with diverse global backgrounds and decades of experience, and are driven by a highly collaborative culture, across products and competencies.
In 2024, Engelhart acquired Trailstone, a global energy trading and technology company.
The acquisition provides us with new expertise, analytics and proprietary technology which is being used to provide risk management and optimisation services to help maximise the value of our clients’ renewable power.
The acquisition also expanded Engelhart’s capabilities into physical natural gas across North America, a critical fuel to support the energy transition.
Our talented and experienced individuals work together according to its four company values: Performance, Agility, Collaboration, Entrepreneurship.

Equal Opportunity

We believe in inclusivity and are therefore dedicated to ensuring all employees – across gender identity, race, ethnicity, sexual orientation, religion, life experience, background and more – feel welcome and included in the company.
We promote diversity because we believe it is essential to our ability to think holistically.