Lead AI Engineer responsible for setting vision, building team, and delivering AI solutions at scale in semiconductor manufacturing.
Responsibilities
Define AI strategy and architecture for integrating machine learning into core engineering and manufacturing processes.
Partner with tool, process, and applications engineers to map as-is processes and define a to-be AI/automation architecture and deliver measurable outcomes.
Ship agentic assistants for use-cases. Stand up LLM + RAG + tool integrations that help engineers accelerate tool operation/setup/maintenance and explain trade-offs.
Lead projects across predictive maintenance, computer vision, LLM-based engineering assistants, process optimization, simulation and digital twins.
Build retrieval-augmented AI assistants to query internal knowledge bases, tools, and logs.
Design and implement end‑to‑end generative AI and non‑AI systems as a full‑stack AI Software Architect, ensuring scalability, security, and cloud‑native best practices.
Responsibilities
Define and design AI and non‑AI components of software systems
Architect solutions that integrate AI capabilities into enterprise systems
Ensure adherence to cloud‑native best practices across AWS and multicloud deployments
Evaluate the best fit for purpose technologies
Ensure newly developed AI applications fit within the context of particular RDT and Business functions
Requirements
B.Sc.
B.Eng.
M.Sc.
M.Eng.
Ph.D. in Computer Science, Software Engineering, Artificial Intelligence or related field
Design, build, and own the infrastructure and workflows behind machine learning and LLM-powered systems, ensuring reliable deployment from experimentation to production.
Responsibilities
Design, deploy, and operate MLOps platform infrastructure, cloud services (AWS, Azure, GCP), and LLM inference pipelines (Ollama, vLLM).
Build automated CI/CD pipelines, IaC, and monitoring/alerting systems to support RAG applications, AI agents, and production deployments.
Manage Linux servers, networking, security endpoints, and troubleshoot production issues alongside AI engineering teams.
Establish department-wide MLOps/DevOps standards, best practices, and reusable infrastructure templates.
Partner with AI Developers and RAG Engineers to guide architecture, deployment strategies, tool selection, and production readiness.
As an Applied Scientist with at least 5 years of experience, you will join a dynamic, multi-disciplinary agile team, using your skills to transform healthcare by delving deep into a variety of data sources.
Responsibilities
Developing and implementing Data Science products, focusing on Natural Language Processing using LLMs and Generative AI tools.
Collaborating with stakeholders to proactively identify relevant LLM/NLP use cases.
Applying your NLP/LLM expertise to create and refine models that meet Roche's business requirements.
Working closely with clients to understand their business needs, gather requirements, and provide consultative insights to inform strategic decisions.
Supporting prioritisation efforts, understanding feasibility and business impact, and making informed decisions in a rapidly changing environment.
Build and deploy agentic generative AI systems and classical ML solutions end‑to‑end, combining full‑stack engineering, MLOps, and data science to deliver production‑ready AI applications.
Responsibilities
Design and build advanced AI agentic systems, state machines, search‑based conversational systems
Develop workflows leveraging Large Foundational Multimodal Models
Implement Model Context Protocol servers/clients
Collaborate with AI Architects, Product Owners, and developers to integrate AI capabilities
Leverage AI coding agents daily to accelerate full‑stack development cycles
Requirements
B.Sc., B.Eng., M.Sc., M.Eng., Ph.D., or equivalent in Computer Science, Physics, Statistics, Mathematics, or a related field
Operating as a Senior AI Systems Engineer setting technical direction across multiple teams, influencing architecture and engineering standards and elevating technical capabilities across the organization to make autonomous vehicles a reality.
Compensation
$185,000 - $215,000 along with an annual corporate bonus, and a long-term incentive bonus
Responsibilities
Setting technical direction across multiple teams
Influencing architecture and engineering standards
Elevating technical capabilities across the organization
Building and enhancing systems with strong leadership skills
Design, implementation, and evaluation of AI-focused systems
Requirements
Master’s degree in Computer Science, Computer Engineering, Robotics, or Electrical Engineering
Ph.D. in Computer Science, Computer Engineering, Robotics, or Electrical Engineering
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