Agentic AI Engineer

About the role

Bitdeer is seeking an Agentic AI Engineer to design and build scalable multi‑agent architectures that automate end‑to‑end engineering and business workflows across its AI cloud platform. You will work at the intersection of large language models, infrastructure automation, and cloud‑native services to create autonomous systems that self‑optimize and self‑heal.

Key responsibilities

• Design and implement multi‑agent systems (planners, executors, judges) for NeoCloud engineering and business processes.
• Integrate AI agents with internal services, SaaS platforms, and APIs to trigger autonomous infrastructure actions such as node cordoning, checkpointing, and hardware remediation.
• Develop agents capable of generating code, refactoring software, and analyzing telemetry to correct system issues in real time.
• Architect short‑term and long‑term memory mechanisms for agents, using protocols like the Model Context Protocol (MCP) to maintain continuity across long‑running tasks.
• Build Retrieval‑Augmented Generation (RAG) pipelines that ground agent decisions in reliable telemetry and observability data.
• Implement “LLM‑as‑a‑Judge” guardrails to evaluate agent performance, reliability, and security compliance.
• Establish Harness engineering practices for automated deployment, governance, and lifecycle management of agentic systems via CI/CD and feature‑flagging.
• Collaborate with Kubernetes and Infrastructure Engineering teams to embed autonomous agents into the platform control plane for proactive resilience.

Required profile

• Bachelor’s or Master’s degree in Computer Science, Artificial Intelligence, Software Engineering, or a related field.
• Minimum 6 + years of software engineering experience with production‑grade Python and TypeScript.
• Extensive experience with modern agentic frameworks such as LangGraph or CrewAI and with frontier LLMs (OpenAI, Anthropic Claude).
• Proven ability to translate ambiguous business processes into robust, reusable AI logic.
• Experience deploying AI features in managed runtimes and cloud‑native environments (AWS, Azure, GCP, or private clouds).
• Familiarity with Kubernetes internals, infrastructure automation, and CI/CD pipelines.

Required skills

• Python
• TypeScript
• LangGraph
• CrewAI
• OpenAI models
• Anthropic Claude
• AWS
• Azure
• Google Cloud Platform (GCP)
• Kubernetes
• Jenkins
• GitHub Actions
• Harness