Posted:4 days ago|
Platform:
Hybrid
Full Time
Build and manage AI/ML infrastructure for model training, deployment, and monitoring.
Develop CI/CD pipelines (using GitHub Actions or equivalent) for continuous integration, model versioning, and automated deployment.
Integrate and manage LangFuse for prompt management, tracing, and observability in LLM workflows.
Implement LangChain pipelines for building and orchestrating LLM-powered applications.
Deploy and serve AI/ML models built using PyTorch and TensorFlow in production environments.
Create FastAPI-based RESTful APIs for AI model inference and service integration.
Design and maintain Vector Databases (such as Pinecone, Weaviate, FAISS, or Milvus) for retrieval-augmented generation (RAG) use cases.
Collaborate with data teams to ensure efficient model lifecycle management (training, validation, deployment, monitoring).
Monitor and optimize model performance, latency, and resource utilization in production.
Implement DevOps and MLOps best practices, ensuring scalability, reproducibility, and observability.
Cloud Platforms: AWS, Azure, or GCP (any major cloud environment)."
Aziro
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