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Job Overview

You'll apply machine learning, LLM, and distributed systems expertise to make agents plan, reason, and use tools reliably, and you'll build the production systems that run those agents at enterprise scale. The systems that make this possible are what you'll build and own.

Key points

  1. Agent orchestration engine — A state machine that manages long-running agent sessions, coordinating planning, execution…

  2. Model routing and adaptation — Choose, route between, and fine-tune or distill LLMs for specific agent tasks. Meet enterprise requirements such as granular permissions…

  3. Distributed session management — Lease-based ownership using conditional writes, heartbeat protocols, and crash recovery via checkpointing.

  4. Event-driven message pipeline — FIFO queues for ordered delivery, consumers for event processing, and real-time streaming via gRPC and Socket.IO.

  5. Distributed systems: consistency models, idempotency, exactly-once delivery, distributed locking/leasing.

  6. Python asyncio, Go goroutines, structured concurrency, cancellation handling.

  7. Event-driven architectures: message queues (SQS, Kafka), pub/sub, backpressure, delivery guarantees.

  8. Database systems for infrastructure: DynamoDB (conditional writes, transactions), Redis (connection pooling, pub/sub).

Skills

LLMsMachine LearningNLPPythonRAG

Experience: Senior-level · 5+ years · Job type: Full-time

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