Senior AI/ Machine Learning Engineer
Tasks
- Drive performance cost and reliability
- Establish reusable substrate for tools and skills
- Evaluate emerging AI techniques
- Mentor engineers and review designs
- Own agent security as a design property
- Own agentic platform end to end
- Own evaluation discipline for the organization
- Partner with Data Science Engineering DevOps and Product
- Set technical direction for agentic systems
Perks/Benefits
- N/A
Skills/Tech-stack
AWS | Agent memory | Azure | CI/CD | Context engineering | Docker | Faiss | FastAPI | GCP | Git | Hugging Face | Inference Optimization | Jenkins | Jira | Kafka | Kubernetes | LLM-as-judge | Langfuse | Langgraph | Langsmith | MLflow | Model selection | OpenTelemetry | PGVector | Pandas | Pinecone | Prompt Caching | Prompt Injection Resistance | Prompt injection | Pulsar | PyTorch | Python | RAG | Ray | Redis | Sandboxing | Scikit-learn | Spark | Structured outputs | Temporal | Token budgeting | Tool permissioning | Vector Databases | We can use W&B | Weaviate
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