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 environments.
- AI/ML: Machine learning basics, model integration, inference workflows
- DevOps: CI/CD, automation, monitoring, troubleshooting.
Preferred Qualifications
- Bachelor’s degree in computer science, Electronics, Instrumentation, or a related technical field; advanced degree is a plus.
- Prior experience in scientific, industrial, or instrument-control software environments is highly desirable
