Live opening · Posted 1 day ago

Coffeebeans.io - AI Engineer - Deep Agent Architectures

CoffeeBeans · Odisha, India (On-site)
Linkedin No
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At a glance

The key details from the original listing.

Posted 1 day ago
CompanyCoffeeBeans
LocationOdisha, India (On-site)
Work modeNo
SkillsPython, TypeScript, AWS, Azure, GCP, Docker, Kubernetes
SourceLinkedin
ListedPosted 1 day ago

About the role

Description supplied by the original job listing.

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)

Work arrangement
No

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