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Coffeebeans.io - AI Engineer - Deep Agent Architectures

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Onsite

Coffeebeans.io - AI Engineer - Deep Agent Architectures

CoffeeBeans

Coffeebeans.io - AI Engineer - Deep Agent Architectures

Skills

Google Cloud PlatformSQL QueriesDockerNodeTypescriptPythonDatadogAIAWSPrometheusKubernetesELK StackAzure

8 days ago

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Important information


Contract type:

Permanent

Location:

Odisha, India

Work mode:

Onsite

Published on:

17 October 2026

What they need


Technical Skills & Requirements AI Agents & Deep Agent Architectures :

  • Frameworks : Expert-level mastery of LangGraph for building complex, cyclic, and stateful agentic workflows.
  • Deep Agents Harness : Hands-on experience implementing the Deep Agents open-source library built on top of LangGraph, leveraging its native architectural patterns.
  • Advanced Reasoning Patterns : Proven experience building agents with built-in dynamic task planning (to-do lists), isolated sub-agent spawning, and persistent virtual file systems within the graph state.
  • State Management & Interruption : Mastery over LangGraph state machines, including context compression middleware and the use of interrupt nodes to manage asynchronous human-in-the-loop (HITL) approval checkpoints.

LLMOps, Evaluation & Guardrails

  • Evaluation Frameworks : Extensive experience with Ragas (Retrieval Augmented Generation Assessment) to programmatically benchmark faithfulness, context recall, and answer relevance.
  • Guardrails & Alignment : Implementation of strict security and behavior firewalls using frameworks like NeMo Guardrails, Guardrails AI, or Llama Guard.
  • LLM Telemetry : Experience mapping evaluation metrics into observability tools such as LangSmith, Phoenix, TruLens, or Arize.

Advanced Retrieval (RAG) & Tool Integration

  • Vector Ecosystems : Experience with vector databases (e.g., Pinecone, Chroma, Milvus, Qdrant) and advanced retrieval techniques (hybrid search, cross-encoder re-ranking).
  • Deterministic Tool Calling : Expert-level Python or TypeScript proficiency to build robust agent function-calling mechanisms, integrating seamlessly with enterprise REST APIs, webhooks, and SQL/NoSQL databases.

Production Deployment & Monitoring

  • Infrastructure : Deep understanding of containerized deployment (Docker, Kubernetes) within enterprise cloud environments (AWS, Azure, or GCP).
  • Auditability : Experience setting up structured logging and immutable telemetry pipelines (Datadog, Prometheus, ELK stack) for complete audit trails.

(ref:hirist.tech)

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