Senior Staff Machine Learning Engineer, AI Agent Platform
USD 130K-300K Senior-level Full Time
Tasks
- Architect enterprise skill ecosystems
- Architect multi agent orchestration
- Build MCP server and registry components
- Build error recovery and session state handling
- Build evaluation frameworks for AI agents
- Build observability pipelines
- Create skill registries and marketplace
- Define AI agent platform technical strategy
- Design AI agent lifecycle management
- Design production AI agent harnesses
- Develop RAG pipelines
- Enforce RBAC and ABAC access controls
- Enforce guardrails for safe autonomous operation
- Establish AI governance frameworks
- Govern internal skill marketplace
- Implement A2A communication infrastructure
- Implement authorization and authentication controls
- Implement context compaction
- Implement human in the loop escalation
- Implement tool dispatch and context management
- Maintain audit trails
- Manage memory hierarchies
- Manage prompt workflows
- Mentor engineers and lead technical initiatives
- Orchestrate agent workflows
- Perform skill level security vetting
Perks/Benefits
- N/A
Skills/Tech-stack
A2A | ABAC | Agent Orchestration | Agent systems | Arize Phoenix | Authentication | Authorization | Autogen | C Sharp | C++ | CI/CD | Context engineering | CrewAI | Elasticsearch | Encryption | Fine Tuning | GPU | Go | Guardrails | Injection Defense | Java | Kafka | Kubernetes | LLM | Langchain | Langfuse | Langgraph | Langsmith | Language Models | Large Language Models | MCP | Model Inference | MongoDB | Monitoring | Multi-Agent | Multi-Agent Systems | Neo4j | Observability | PII Detection | PostgreSQL | Prompt injection | Prompt injection defense | Python | Qdrant | RAG | RBAC | Ray | Redis | Security vetting | Spark | Temporal | Testing
Education
Bachelor of Engineering | Bachelor of Science | Master of Science | PhD
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