Senior AI/ML Engineer
Tasks
- Apply compliance by design for ph i pi i handling
- Automate data and model pipelines for mlops
- Build agent orchestration for single and multi agent systems
- Build reusable apis and sdks for ai workloads
- Create llm runtimes with caching and token governance
- Create policy as code in cicd
- Deploy machine learning models in production
- Design and build generative AI platforms
- Develop vector search and embedding stores
- Implement encryption access controls and audit trails
- Implement evaluation and experimentation pipelines
- Implement rag ingestion and retrieval pipelines
- Implement safe tool use and guardrails
- Integrate observability for performance drift and safety
- Lead architecture reviews and code reviews
- Mentor engineers on distributed systems and agentic ai
- Monitor bias explainability and regression detection
- Provision cloud infrastructure for model training and serving
- Set up model and endpoint registry
- Use Terraform for infrastructure automation
Perks/Benefits
- 401k plan
- Commuter benefits
- Dental insurance
- Disability plans
- Fertility care benefits
- Free testing
- Life insurance
- Medical insurance
- Pregnancy and baby bonding leave
- Referral program
- Vision insurance
Skills/Tech-stack
AWS | Access Control | Agent Orchestration | Audit Logging | CI/CD | Caching | Cost Optimization | Datadog | Distributed Systems | Embeddings | Encryption | Experimentation | Feature Stores | Guardrails | Kubeflow | Kubernetes | LLMOps | Latency optimization | MLflow | Model routing | Multi-model | Multi-model routing | Multi-region | Multi-region resilience | Observability | Policy-as-Code | Prompt Tuning | PyTorch | Python | Retrieval-Augmented Generation | Spark | TensorFlow | Terraform | Token governance | Tool use | Vector Databases | Vector Search | “as-code”
Education
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