Offer summary

Qualifications:

Masters degree in Computer Science or a related field., 3-5+ years of professional software engineering experience., Proficiency in Python with clean, modular coding skills., Knowledge of software architecture, cloud platforms, and containerization..

Key responsibilities:

Build and maintain software tools and data pipelines for machine learning.

Collaborate with data scientists to deploy models and deliver solutions.

Ensure performance and reliability of applications through troubleshooting and improvements.

Manage multiple projects focusing on delivering business value.

Job description

About Our Client

Our client is a global player in the energy and commodities sector, with a growing investment in data-driven technologies and digital infrastructure. Operating across multiple markets, the company fosters a collaborative and high-performance environment, where innovation, ownership, and technical excellence are highly valued. Employees are encouraged to push boundaries and contribute to projects that directly impact real-world energy flows.

Core Responsibilities

Build and maintain software tools and data pipelines for machine learning applications

Collaborate with data scientists to deploy models and deliver solutions to business teams

Provide expertise in software design, code quality, and architecture (primarily in Python)

Own and evolve key technical infrastructure across the ML lifecycle

Troubleshoot and improve bespoke applications, ensuring performance and reliability

Engage in code reviews, experiment design, and tooling discussions to drive team quality

Manage multiple projects and priorities with a focus on delivering business value

Qualifications

Required:

3-5+ years of professional experience in software engineering

Advanced proficiency in Python and clean, modular code design

Masters degree in Computer Science or related field

Strong understanding of software architecture and delivery risk

Familiarity with cloud platforms (AWS, Azure, or GCP), APIs, and containerization (Docker)

Experience with CI/CD pipelines and software development lifecycle tooling

Excellent communication skills (written and verbal) and collaborative mindset

Preferred:

Experience with machine learning workflows and infrastructure

Exposure to big data tools (e.g., Spark) and data orchestrators (Airflow, Dagster)

Knowledge of commodities or financial trading environments

Interest in LLMs and large-scale model deployment

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