Applied AI Engineer
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Job Description
About Us
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.
About the Role
As a member of the Tech Data, AI & UX team allocated to European & Asian Power Trading in Berlin, you will build the services, infrastructure, and AI & data platforms that power the business, enabling teams to improve their data-driven decisions and connect innovative products.
As part of this Joint Venture, the Applied AI Engineer will help to evolve ECTP’s AI & Agentic platforms – a strategic, firm-wide initiative led by the Tech Data, AI & UX team. The role provides hands-on exposure to the cutting edge of enterprise AI, working alongside experienced data engineer, architects and quant developers in a live, production-grade environment.
You will work closely with trading desk and Technology teams to build core agentic infrastructure, integrate tooling via AWS Lambda and MCP servers, develop agent skills, and configure knowledge bases across user, chat, agent, and application contexts. You will explore and evaluate open-source and proprietary connectors within the rapidly evolving LLM and Generative AI ecosystem, and help roll out individual productivity tools such as Microsoft Copilot and Claude Code. All solutions will be hosted securely within Engelhart’s internal network, with models deployed in a dedicated VPC and governed access to firm data, with a strong preference for AWS-native and Bedrock-based architectures.
This will be a full-time role, owning the following responsibilities:
- Collaborate with the trading desk to supercharge their research capabilities and data-driven insights by extending Engelhart's core AI and agentic platform to address new trading desk requirements.
- Assist in the set-up and configuration of core agentic AI infrastructure, including orchestration layers, agent runtimes, and supporting services, deployed securely within Engelhart’s AWS VPC.
- Build and maintain agent tooling integrations via AWS Lambda functions and MCP (Model Context Protocol) servers, enabling agents to interact with firm data, APIs, and internal systems.
- Develop agent skills and configure knowledge bases, including document stores, vector databases (e.g. Postgres with pgvector, AWS Bedrock Knowledge Bases), and retrieval pipelines for user, chat, agent, and application contexts.
- Research, evaluate, and help integrate open-source and proprietary accelerators, connectors, and frameworks (e.g. LangChain, LlamaIndex, n8n, etc) into the platform, assessing fitness for purpose in a regulated, IP-sensitive environment.
- Support the deployment and end-user adoption of individual AI productivity tools, including Microsoft Copilot and Claude Code, ensuring secure configuration and appropriate access controls within Engelhart’s internal network.
- Contribute to technical documentation, architecture diagrams, and internal knowledge sharing on AI tooling, design decisions, and implementation patterns to support team learning and continuity.
- Collaborate with engineers, data scientists, and business stakeholders to iteratively test and improve agentic workflows, gathering feedback and translating it into technical improvements.
About You
This person will be a technically minded and genuinely curious person who is excited about the transformative potential of AI and wants to go beyond surface-level tooling – you want to understand how things work and help build them. You are comfortable navigating ambiguity, energised by fast-moving technology landscapes, and take pride in writing clean, well-structured code. You thrive in collaborative environments and are motivated by the opportunity to contribute to something that has real, visible impact within a live business setting.
The following education, experiences and skills are essential for application:
- Background in Computer Science, Software Engineering, AI, Data Science, or related technical undergraduate degree.
- A genuine passion for AI, Large Language Models, and Generative AI. Not just as a user, but with curiosity about how they work and how to build with them.
- Proficient in Python, with hands-on experience writing scripts, working with APIs, or building small applications – whether through coursework, personal projects, or hackathons.
- Familiarity with cloud concepts or services (preference for AWS), containerisation (Docker), or REST APIs – even if only at an introductory or project level.
- A proactive, self-directed learning style – able to research unfamiliar technologies, synthesise documentation, and form a view on trade-offs with limited guidance.
- Good communication skills, with the ability to explain technical concepts clearly to both technical and non-technical colleagues, and to work effectively in a small, fast-moving team.
- A collaborative, team-oriented mindset, with the confidence to contribute ideas, ask questions, and engage actively with engineers, data scientists, and business users.
- An organised approach to work, with attention to detail, a strong sense of ownership, and the ability to manage multiple tasks and priorities in a fast-paced environment.
- Comfortable working with ambiguity and rapid change, with the resilience to iterate quickly, adapt to new findings, and thrive in a technology landscape that evolves week to week.
In addition, the following criteria is not required, but highly desirable for this role:
- Foundational knowledge of energy trading markets, including core concepts like balancing mechanism operations, Power Purchase Agreements (PPAs), and Virtual Power Plants (VPPs).
- Prior exposure to LLM frameworks such as LangChain, LlamaIndex, or similar; experience with vector databases (e.g. pgvector, Pinecone, Chroma); or familiarity with AWS Bedrock, SageMaker, or equivalent managed AI services.
- Experience with version control (Git) and basic understanding of CI/CD pipelines or infrastructure-as-code (e.g. AWS CloudFormation, CDK).
- Understanding of prompt engineering techniques, retrieval-augmented generation (RAG), or multi-agent system design patterns.
- Participation in AI/ML hackathons, open-source contributions, personal projects involving LLMs or automation, or active engagement with the AI development community (e.g. Hugging Face, GitHub, LangChain forums).
- Portuguese as a second language.
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