Job DescriptionYou will join the Digital Services team to architect, prototype and productionize Generative AI solutions that add value across companies instrument and digital product portfolio (e.g., data-driven insights from analyzers, intelligent documentation, RAG chatbots for product support).
You’ll work end-to-end, data pipelines, model training/fine-tuning, MLOps and API deployment.
Key responsibilities -
Design and implement GenAI/NLP solutions (chatbots, summarization, extraction, RAG pipelines).
Build data ingestion and preprocessing pipelines for instrument telemetry, lab results and business data.
Fine-tune and evaluate LLMs using frameworks such as Hugging Face Transformers, and implement prompt engineering best practices.
Implement vector search + RAG workflows using vector DBs (FAISS, Weaviate, Pinecone or similar).
Package models as microservices and deploy to cloud (AWS/GCP/Azure) or on-premise as required.
Collaborate with product, hardware and QA teams to integrate AI features into web/mobile and embedded systems.
Create monitoring, logging and retraining strategies (ML monitoring, data drift detection).
Produce technical documentation and train internal stakeholders on solution usage.
Requirements
Minimum 5 years hands-on GenAI/NLP/LLM work
Strong Python expertise; hands-on with PyTorch and/or TensorFlow.
Experience with Transformers (Hugging Face), LLM fine-tuning and prompt engineering.
Familiarity with vector databases and retrieval augmentation (RAG).
Experience deploying models as scalable APIs (FastAPI/Flask + Docker + Kubernetes).
Solid data engineering skills (ETL, SQL, pandas, streaming an advantage).
Experience with MLOps tools (MLflow, DVC, Kubeflow, or equivalent).
Good software engineering practices (unit tests, CI/CD, code reviews).