Responsibilities
- Analyze complex manufacturing datasets to uncover patterns and inform model design
- Build and iterate machine learning models to solve targeted business and engineering challenges
- Develop and maintain scalable data and solution pipelines supporting training and deployment
- Deploy, monitor, and optimize ML pipelines and models in cloud environments
- Find opportunities to integrate Generative AI into platforms and products
Basic qualifications
- Master’s degree in Computer Science, Machine Learning, Data Science, Statistics, or a related field (or equivalent experience)
- 2+ years building and deploying end‑to‑end machine learning systems in cloud platforms (GCP or snowflake or AWS)
- 2+ Hands-on experience with ML frameworks such as TensorFlow, PyTorch, or scikit‑learn
- 2+ Strong programming skills in Python or Java and proficiency in SQL
- 2+ Experience with Docker, Kubernetes, and event‑driven pipelines using systems like Pub/Sub, Solace, or Kafka, GKE
- 2+ Experience developing ETL/ELT pipelines with Kubeflow, Dataflow, and/or Airflow, and/or NIFI
- 2+ Knowledge of NLP, prompt engineering, model evaluation, and LLM fine‑tuning
- 2+ Working knowledge of databases such as BigQuery, Snowflake, MSSQL, or PostgreSQL
Preferred qualifications
- PhD or equivalent experience in a field related to AI or Machine Learning
- Experience building Generative AI solutions using frameworks such as ADK, LangChain/LangGraph, CrewAI, DsPy, or Semantic Kernel
- Experience developing RAG, GraphRAG, or semantic search systems
- Experience with computer vision, signal processing, or advanced ML algorithms
Tags & focus areas
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