ML Ops Engineer

Entangle is reshaping the blockchain landscape with advanced interoperability and data connectivity solutions. By creating a seamless framework for DApps to interact across various networks, we’re solving one of blockchain’s biggest challenges: the barriers between fragmented ecosystems. Our Photon Messaging Protocol, universal data feeds, and customizable agent network bring high-performance, scalable infrastructure to developers in the DeFi space and beyond.

We are currently seeking an experienced ML Ops engineer with experience in AI projects to join our team.

Key Responsibilities:

1. Model Deployment
   • Dockerize and deploy multi-agent LLM instances, ensuring compatibility with LangGraph pipelines.
   • Optimize model serving for low-latency, high-throughput applications, leveraging GPU/TPU resources efficiently.
   • Implement scalable model deployment strategies for concurrent multi-agent tasks.
2. Monitoring and Logging
   • Establish end-to-end monitoring for the AI platform, tracking system health, latency, and failure rates.
   • Monitor LLM-specific metrics such as token latency, memory consumption, and prompt-response accuracy.
   • Develop drift detection mechanisms for model inputs and outputs to ensure sustained performance.
3. Pipeline Automation
   • Automate training, evaluation, and deployment workflows for LangGraph-enabled pipelines.
   • Build and maintain CI/CD pipelines for integrating multi-agent frameworks with backend services.
   • Automate versioning and rollback mechanisms for LLMs, ensuring seamless updates.
4. Infrastructure Management
   • Collaborate with DevOps teams to scale Kubernetes clusters for LangGraph chains and WebSocket-heavy APIs.
   • Optimize resource allocation for shared GPU/TPU inference loads across agents.
   • Implement caching strategies for high-reuse LLM queries and shared agent tools.
5. Collaboration and Documentation
   • Document multi-agent system workflows, LangChain integration, and API usage for internal and external stakeholders.
   • Collaborate with backend engineers to align model endpoints with application requirements and QA teams to resolve deployment issues.
   • Provide guidelines for extending LangGraph tools and chains with custom agent implementations.

Qualifications:

• Experience deploying (Ray Serve/vllm/...) and optimizing (quantization/ONNX/...) LLMs in production (e.g., OpenAI, HuggingFace models).
• Proficiency with containerization (Docker) and orchestration (Kubernetes, Helm).
• Familiarity with LangChain, Web3 tools, or multi-agent systems is a strong plus.
• Strong understanding of CI/CD tools (e.g., GitHub Actions, Jenkins).
• Excellent communication skills and ability to document complex systems clearly.

What we offer:

• Exciting growing international start-up with ambitious goals of making headways in a revolutionary, multi-billion dollar industry.
• Pay in USDT.
• Scrum/agile environment.
• Highly skilled engineering team.
• Attractive compensation plus token allocations.
• Remote work in a timezone that corresponds well with the UAE time.
• Paid vacation and public holidays.