Engineer III Senior Software Engineer

Work Schedule

Standard (Mon-Fri)

Environmental Conditions

Office

Job Description

Job Description

When you join us at Thermo Fisher Scientific, you will become part of a highly collaborative team that is passionate about building software platforms that power advanced scientific instruments and solutions. With industry-leading investment in innovation, we provide our teams with resources and opportunities to make meaningful contributions to the world.

How will you make an impact

As a Senior Engineer in the AIP1 (AI Platform One) team you will contribute to developing and maintaining a comprehensive platform designed to streamline the lifecycle of AI models, from training and dataset management to deployment and inference.

We are seeking an experienced Senior Software Engineer with 6 10 years of experience and strong hands-on expertise in Python and C++. The ideal candidate should have a solid understanding of machine learning concepts, along with practical experience in OS-level virtualization, containerization, Kubernetes, and microservices-based architecture.

In this role you will work on building scalable engineering platforms, automation frameworks, and infrastructure components that support AI/ML-enabled applications, edge workloads, and cloud-native services. You will contribute to software design, system integration, and engineering best practices, while also supporting mentoring, collaboration, and continuous improvement across the team.

What will you do

• Design, develop, and maintain software components using Python and C++.
• Build and optimize backend services, automation tools, and platform-level components.
• Work with OS-level virtualization technologies to support isolated execution environments and workload management.
• Develop and maintain containerized applications using Docker.
• Deploy, manage, and troubleshoot services running on Kubernetes.
• Design and implement microservices-based solutions with clear API boundaries and service ownership.
• Collaborate with AI/ML teams to integrate machine learning models into software workflows and production systems.
• Support model deployment, inference workflows, and performance optimization where required.
• Work with CI/CD pipelines for automated build, test, deployment, and release processes.
• Troubleshoot system-level, container-level, and service-level issues.
• Ensure software quality through code reviews, unit testing, integration testing, and documentation.
• Collaborate with cross-functional teams across software engineering, DevOps, infrastructure, cybersecurity, and product teams.
• Work effectively within an Agile Scrum / SAFe framework, participating in backlog refinement, sprint planning, technical estimation, PI planning, reviews, retrospectives, and cross-team coordination to support predictable and high-quality delivery.

Required Skills

• 6- 10 years of experience in software development or platform engineering.
• Strong programming experience in Python.
• Hands-on development experience in C++.
• Strong understanding of software engineering principles, object-oriented design, design patterns, and development methodologies, with experience in multi-module application development .
• Working knowledge of machine learning concepts, model workflows, and inference pipelines.
• Experience with OS-level virtualization such as KVM, QEMU, Hyper-V, VMware, or similar technologies.
• Hands-on experience with Docker for containerizing applications.
• Practical experience with Kubernetes for deployment, scaling, and service orchestration.
• Experience designing or working with microservices architecture.
• Understanding of REST APIs, service communication, logging, monitoring, and debugging.
• Experience with Linux environments and shell scripting.
• Familiarity with CI/CD tools and automation workflows.
• Ability to troubleshoot complex software and infrastructure issues.

Desired Skills

• Experience deploying or integrating ML models into production or edge environments.
• Understanding of GPU acceleration, CUDA, or AI inference runtimes is a plus.
• Experience with cloud-native development practices.
• Knowledge of observability tools such as Prometheus, Grafana, ELK, or similar.
• Experience with infrastructure-as-code tools such as Terraform, Ansible, or Helm.
• Familiarity with message queues or event-driven architectures.
• Experience working in regulated or enterprise engineering environm…