Job Description :
Work Schedule
Standard (Mon-Fri)
Environmental Conditions
Office
As part of the Thermo Fisher Scientific team, you'll discover meaningful work that makes a positive impact on a global scale. Join our colleagues in bringing our Mission to life every single day to enable our customers to make the world healthier, cleaner and safer. We provide our global teams with the resources needed to achieve individual career goals while helping to take science a step beyond by developing solutions for some of the world's toughest challenges, like protecting the environment, making sure our food is safe or helping find cures for cancer.
Description
We are seeking an experienced AI Architect to design and deliver advanced AI/ML solutions that drive automation, optimization, and data-driven decision-making. This role will focus on building scalable AI architectures, leading end-to-end AI/ML initiatives, and enabling enterprise-wide adoption of intelligent solutions.
Experience Required
- 10+ years of overall professional experience
- 5+ years of experience as an AI Architect or similar role
- Proven experience in designing and implementing at least 3-4 AI/ML initiatives end-to-end
Key Responsibilities
- Define architecture and roadmap for AI and intelligent applications across the organization
- Design and implement Retrieval-Augmented Generation (RAG) based AI solutions for enterprise use cases
- Design and implement scalable, robust, and reusable AI/ML solutions
- Drive end-to-end AI/ML solution design from ideation to deployment and scaling
- Identify opportunities for automation and AI adoption across business functions
- Ensure scalability, reliability, and performance of AI and automation solutions
- Collaborate with business stakeholders, data scientists, and engineering teams to deliver impactful solutions
- Translate business requirements into technical architecture and solution design
- Promote Agile/Scrum practices for effective and timely delivery
- Drive innovation through adoption of emerging technologies including IoT and digital twins
Required Skills & Qualifications
- Strong expertise in AI/ML and Generative AI with proven delivery at scale ability to translate business problems into production-grade solutions and measurable outcomes (value, ROI)
- Cloud-native architecture experience on AWS/Azure with Kubernetes (EKS/AKS) and CI/CD (GitHub Actions, Azure DevOps, Jenkins)
- Solid data platform engineering background: Databricks Lakehouse and/or cloud storage (S3 or equivalent), and scalable data processing/pipeline design
- Hands-on experience across the Python ecosystem (Python, Pandas, NumPy) and ML frameworks (Scikit-learn, XGBoost, PyTorch, TensorFlow) with familiarity in experiment tracking and model registry
- Deep experience with GenAI/LLM ecosystems: providers (Azure OpenAI/OpenAI, Anthropic Claude, AWS Bedrock), orchestration (LangChain, LlamaIndex, Semantic Kernel), and agent frameworks (LangGraph, CrewAI, AutoGen)
- Practical expertise in RAG architectures end-to-end: ingestion pipelines, chunking/metadata enrichment, embeddings, vector retrieval, reranking, grounding/citation
- Experience with embeddings (OpenAI, Cohere, Bedrock Titan, sentence-transformers) and vector databases (Pinecone, Weaviate, Milvus, pgvector, OpenSearch vector engine)
- Proficiency in prompt and workflow orchestration (prompt templates, guardrails, tool/function calling) and integrating AI into enterprise architectures
- Strong grasp of security, governance, and compliance: IAM (Azure AD/Entra ID, AWS IAM, RBAC/ABAC), secrets management (KMS, Key Vault, HashiCorp Vault), and data privacy/regulatory standards
- Understanding of AI governance (model risk, prompt/output filtering, human-in-the-loop, audit logging, data lineage, responsible AI)
- Experience with observability, reliability engineering, and FinOps for data/AI platforms
Preferred Qualifications
- Experience with microservices frameworks such as FastAPI
- Familiarity with API patterns such as REST and GraphQL
- Experience or understanding of UI/UX design principles and collaboration with design teams
- Exposure to IoT and digital twin technologies
- Certifications in AI/ML, cloud (AWS/Azure), or data engineering
- Knowledge of GxP processes and regulated environments
Key Competencies
- Strong architectural thinking and solution design
- Innovation and continuous improvement mindset
- Strong problem-solving and decision-making skills
- Ability to manage complex, cross-functional programs
Education
- Bachelor's or Master's degree in Computer Science, Engineering, Data Science, or related field
Why Join Us
- Opportunity to lead enterprise-scale AI and automation transformation
- Work on cutting-edge intelligent applications and emerging technologies
- Collaborative and innovation-driven environment
- Strong leadership and career growth opportunities
