ML Engineer

Design and implement distributed microservices for AI agent orchestration

Build and optimize task graph execution engines for LLM-powered workflows

Develop integrations with multiple LLM providers and tool ecosystems

Create and maintain gRPC/REST APIs for real-time AI interactions

Implement document processing pipelines (ingestion, chunking, vector embeddings)

Work on knowledge base systems with vector search capabilities

Build code interpreter sandboxes for safe AI code execution

Contribute to MCP (Model Context Protocol) server implementations

Python - 3+ years production experience, async programming (asyncio), type hints, Pydantic

Backend - FastAPI or similar frameworks, gRPC Protobuf, microservices patterns

Databases - PostgreSQL (SQLAlchemy, migrations), Redis (caching, pub/sub, distributed locks)

AI/ML - LLM API integration (OpenAI, Google AI, Anthropic), understanding of embeddings RAG

Infrastructure - Docker, Kubernetes basics, observability (logging, tracing)

Cloud - GCP, AWS, or Azure experience

Vector databases (Milvus, Pinecone)

Workflow orchestration (Temporal, Celery)

Document processing (PDF parsing, OCR)

Bazel build system

MCP (Model Context Protocol)