Capgemini Engineering Ukraine

You will join the Advanced Analytics team of a leading multinational energy company, working on one of the most technically demanding applications of machine learning in the engineering domain. The mission is to augment the upstream design configuration process for complex mechanical equipment - reducing manual iteration cycles before simulation software is ever invoked. This role sits at the intersection of applied ML and mechanical engineering, working directly with senior engineering stakeholders on highly bespoke, domain-specific datasets.

What You Will Do

· Analyze and model FEA-derived engineering datasets - load cases, material properties, geometric configurations - to build ML-based surrogate models that predict optimal design configurations.

· Apply uncertainty quantification techniques to assess model confidence and communicate prediction reliability to engineering stakeholders.

· Implement active learning strategies to intelligently prioritize the most informative simulation runs, minimizing the number of costly iterations required to train robust models.

· Collaborate closely with mechanical engineers and product management to translate domain constraints into well-defined ML problem formulations.

· Select, justify and implement appropriate ML approaches - regression, ensemble methods, neural networks based on dataset characteristics, and defend those choices with technical rigor.

· Build, validate and iterate on models using Python in an AWS environment.

· Work semi-autonomously: take high-level requirements, structure the problem independently and drive execution with minimal oversight.

Mush Have

· Mechanical or physical engineering domain experience - hands-on exposure to engineering datasets involving loads, stress, pressure, material properties or structural behavior. Aerospace, subsea, automotive and heavy industry backgrounds are all relevant.

· FEA dataset fluency - you understand what simulation output data looks like, how it is structured and what the physical quantities represent. Operational knowledge of ANSYS or Abaqus is not required, but you must have worked with data generated by tools of this kind.

· Strong ML foundations - deep grounding in mathematics and statistics. You can justify algorithm selection, explain model behavior and diagnose failure modes. You do not treat ML as a black box.

· Uncertainty quantification - applied UQ experience in an engineering or scientific context.

· Active learning - practical experience designing or implementing active learning loops to reduce the number of expensive simulations runs needed to train reliable models.

· Production-quality Python — clean, maintainable ML code built for real engineering workflows.

· Autonomous working style — you can take high-level requirements, structure the problem independently and deliver without daily oversight.

What Will Set You Apart

· Experience with surrogate modelling, design space exploration or simulation data regression.

· Familiarity with FEA/CFD simulation workflows (ANSYS, Abaqus, Fluent) - you do not need to operate these tools, but understanding the workflow is a meaningful advantage.

· Background in physics-informed ML or scientific computing.

· AWS experience for model development and deployment.

What you will love about working here?

We care about all our employees and want them to feel as comfortable as possible. That's why we offer them health insurance from the first days, regardless of the probationary period.
The gift from the company - Christmas holidays from 25 December to 31 December.
Сooperation with Superhumans center and Veteran HUB. Capgemini Engineering has supported the launch of psychological rehabilitation department of Superhumans. Our team also donnated over UAH 500 000 prosthetics for three Ukrainian defenders. Currently, we support psychological counseling provided by the Veteran Hub, and we have implemented a internal policy making the company friendly to military and veterans with the assistance of the Hub.

MUST HAVE SKILLS TO HAVE COMMERCIAL EXPERIENCE WITH: Go, Kubernetes, Docker, Kubebuilder or Operator SDK

Responsibilities

• Design, develop, and maintain backend services using Go
• Build and support cloud-native applications running on Kubernetes
• Develop custom Kubernetes operators/controllers for automation and platform management
• Design and implement APIs, microservices, and integrations with internal/external systems
• Participate in architecture design and technical decision-making
• Optimize application performance, reliability, and scalability
• Troubleshoot production issues and support platform improvements
• Collaborate with DevOps, platform, and product teams
•  

Required Skills

• Strong hands-on experience with Golang
• Solid experience with Kubernetes
• Proven experience in developing Kubernetes Operators using frameworks such as Kubebuilder or Operator SDK
• Good understanding of containers, Docker, and cloud-native architecture
• Experience with microservices and REST/gRPC APIs
• Familiarity with CI/CD pipelines

MUST HAVE SKILLS TO HAVE COMMERCIAL EXPERIENCE WITH: SDN (Software defined networking) ,  kubernetes network infrastructure design.

Tasks and Responsibilities

Design, setup and management of a software defined network (incl. software and hardware)
Design, setup and management of kubernetes based infrastructure
Ensure adherence of network to guiding security guidelines (esp. IEC-62443-3)
Design hybrid and cloud networking setup

Hard Skills

Hard Skills ordered by priority - Software defined networking and kubernetes network infrastructure design are mandatory skills

Software-Defined Networking (SDN)

• Proficiency in SDN technologies, including design, deployment, and management (e.g., VMware NSX, Cisco ACI, Open vSwitch).
• Experience integrating software-defined networking in cloud-native, Kubernetes/OpenShift environments.
• Knowledge of SDN management, programmability, and automation techniques.

Kubernetes Network Infrastructure Design & Management

• Expert in designing and managing enterprise-grade kubernetes network infrastructures.
• Skilled in configuring and managing network equipment (switches, routers, firewalls, load balancers) for containerized platforms
• Experienced with resilient network designs (High Availability, Load Balancing, Redundancy) for kubernetes

Security & Compliance

• Experience implementing network security standards, including Zero Trust architecture, secure segmentation, and firewall rules.
• Familiarity with cybersecurity standards (IEC 62443, NIST, ISO 27001, GDPR).
• Ability to secure networks, implementing robust firewall configurations, encryption (TLS/IPSec), and network access control.

Hybrid, Cloud and on premise networking setups

• Skilled in designing network topologies for hybrid-cloud deployments, ensuring seamless connectivity between cloud (Azure/AWS) and on-premise environments.

• Competence in deploying secure VPNs, firewalls, and gateways for hybrid scenarios.

   

Soft Skills

• Team player and collaborater within the team and between teams
• Ability to support requirements team in defining the correct solution