GCP AI/ML Engineer

Role : GCP AI/ML Engineer  
Location : Remote  
Key Responsibilities
Model Development & Training: Develop and train predictive and generative AI models using Python and frameworks such as TensorFlow, PyTorch, or Scikit-learn, often within Vertex AI.
GCP Implementation: Implement solutions using GCP services like BigQuery, Dataflow, Cloud Functions, and Vertex AI Pipelines to build scalable infrastructure.
MLOps and Automation: Design and automate MLOps pipelines (training, deployment, monitoring) to ensure model performance, scalability, and reliability.
Data Engineering: Construct data pipelines for ingestion, preprocessing, and storage of structured/unstructured data using SQL and BigQuery.
Generative AI Integration: Implement LLMs, retrieval-augmented generation (RAG) patterns, and agentic workflows (e.g., using LangChain).
Optimization & Troubleshooting: Monitor and optimize deployed models for accuracy, latency, and cost-effectiveness. 
Required Skills and Qualifications
Experience: 5+ years in AI/ML model deployment and software engineering.
Technical Proficiencies: Strong programming skills in Python and SQL.
GCP Expertise: Proven experience with Google Cloud Platform, specifically Vertex AI, Dataflow, and BigQuery.
ML Frameworks: In-depth knowledge of TensorFlow, PyTorch, or Scikit-learn.
DevOps/Containerization: Proficiency with Docker, Kubernetes (GKE), and CI/CD tools.