Senior ML Engineer - Agentic AI
USD 140K-150K Senior-level Full Time
Tasks
- Build ETL pipelines for agent knowledge bases
- Build MCP servers for structured tool access
- Build and iterate AI agents end to end
- Create tool schemas for reliable LLM invocation
- Deploy agent services on Kubernetes with Docker
- Design autonomous agent planning and execution
- Design memory architectures for short term long term and episodic recall
- Develop CI CD pipelines for agent services
- Implement evaluation pipelines for agent performance
- Implement security measures prompt injection mitigations and IAM authentication
- Implement system prompts guardrails and few shot examples
- Integrate tool execution via REST APIs and enterprise data sources
- Integrate vector databases for RAG pipelines
- Optimize LLM inference for cost latency and reliability
Perks/Benefits
- 401-K matching
- Dental insurance
- Employee assistance program
- Fitness club discounts
- Flexible paid time off
- Hybrid work model
- Medical insurance
- Paid Holidays
- Parental leave
- Professional development reimbursement
- Sick leave
- Travel discounts
- Vision insurance
Skills/Tech-stack
A/B | A/B Testing | Anthropic API | Autogen | B testing | CI/CD | CrewAI | Docker | ETL | Embeddings | FastAPI | FastMCP | GitHub Actions | Helm | Hugging Face | IAM | Infrastructure as Code | JavaScript | Jenkins | Jitter Optimization | Kubernetes | LLM APIs | Langchain | Langfuse | Langgraph | Language Models | Large Language Models | Llamaindex | MCP | Machine Learning | Milvus | MongoDB | MySQL | Neo4j | Observability | Ollama | OpenAI API | PGVector | PostgreSQL | Prompt engineering | Prompt injection | Prompt injection mitigation | Python | RAG | REST API | React | Semantic Kernel | Similarity Search | Tokenization | Transformers | TypeScript | Vector Databases | Vector similarity | Vector similarity search | “as-code”
Education
N/A
Regions
Countries
States
Cities
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