Staff Engineer, Artificial Intelligence

Job description

What will you do

We are looking for an experienced AI/ML Architect to design and lead the implementation of advanced AI and machine learning solutions. This role involves defining scalable AI/ML architectures, optimizing model performance, and collaborating with different business units and divisions in Thermo Fisher to integrate AI capabilities into business applications. The ideal candidate will have a strong background in machine learning, cloud computing, and software architecture.

Job Functions

• Design and architect end-to-end AI/ML solutions, ensuring scalability, security, and efficiency.
• Guide data scientists and engineers in developing, training, and deploying machine learning models.
• Define best practices for MLOps, including model versioning, monitoring, and retraining strategies.
• Develop AI frameworks and reusable components to accelerate AI adoption across the organization.
• Collaborate with stakeholders to understand business requirements and align AI solutions accordingly.
• Optimize data pipelines and AI infrastructure to support high-performance model training and inference.
• Evaluate emerging AI technologies and recommend suitable tools, frameworks, and methodologies.
• Ensure compliance with AI ethics, governance, and data privacy regulations.
• Implement microservices architecture to build scalable and resilient software solutions.
• Use Cloud platforms like AWS, Azure to deploy and run software applications.

How will you get here

Education:

• Bachelor's/Master's/Ph.D. in Computer Science, Artificial Intelligence, or a related field

Experience:

• 8+ years of experience in AI/ML engineering, including at least 3 years in an architectural role
• Extensive experience in AI/ML model development, deployment, and lifecycle management.
• Expertise in machine learning frameworks (TensorFlow, PyTorch, Scikit-learn) and cloud platforms (AWS, GCP, Azure).
• Strong programming skills in Python, Java, or C++
• Proficiency in MLOps tools (Kubeflow, MLflow, Airflow, Docker, Kubernetes).
• Deep understanding of distributed computing, big data technologies (Spark, Hadoop), and scalable data pipelines.
• Experience with NLP, deep learning, reinforcement learning, or generative AI.
• Experience in AI-driven business transformation and enterprise AI strategies.
• Familiarity with edge AI, IoT, or real-time AI processing.
• Knowledge of ethical AI frameworks and responsible AI principles.
• Strong problem-solving skills and ability to mentor AI/ML teams.
• Experience in agile development methodologies to deliver solutions and product features.