Senior Principal Machine Learning Engineer

Level framing: Recognized expert who mentors others; makes key technical decisions impacting multiple teams; leads resolution of highly complex challenges; cross-functional influence; may engage with external partners as an authority.

Core Responsibilities

• Design and build production backend systems for agentic AI applications — API layers, backend-for-frontend (BFF) patterns, session/state management, and streaming for concurrent multi-user workloads.
• Lead resolution of highly complex, cross-system technical challenges.
• Set the technical bar for hands-on engineering practice across the team.
• Own GitOps and engineering-governance standards across the team (version control, CI/CD, review, promotion).
• Make key MLOps/platform decisions that affect multiple teams (e.g. deployment topology, observability strategy).
• Drive triage and resolution of complex, cross-system production incidents.
• Own architecture for agentic AI systems, including routing, policy enforcement, and RAG/knowledge-registry design.
• Lead evaluation and adoption of new agent frameworks, LLMOps practices, and GenAI tooling across the team.
• Implement authentication/authorization flows for agentic systems (OAuth/OIDC, on-behalf-of token exchange, secure credential storage).
• Mentor other engineers on architecture, design patterns, and production engineering practices, and act as a role model for engineering rigor across the wider team.

Key Tools & Technologies

• Cloud & Data Infra: AWS (EC2/ECS, S3, Lambda, IAM, CloudWatch or equivalent); Databricks & Unity Catalog
• Foundational: Python; Git/GitHub; software engineering best practices; testing; SQL
• MLOps & Deployment: Docker, Kubernetes, Prefect; CI/CD (GitHub Actions); production monitoring; model versioning & lineage; GitOps governance
• GenAI & Agentic Architecture: Claude or comparable LLMs; LangGraph or comparable agent frameworks; RAG architectures; vector databases (e.g. Pinecone); prompt engineering & evaluation

Required Qualifications

11–15 years of hands-on experience, with demonstrated growth into architecture-level ownership spanning multiple teams or systems.

• Strong proficiency in Python and a track record of writing clean, testable, production-quality code.
• Demonstrated experience owning CI/CD, containerisation, and orchestration for production ML/AI systems.
• Proven experience developing or deploying LLM-based applications, including prompt engineering, RAG, or agentic workflows.
• Strong working knowledge of containerisation (Docker), orchestration (Kubernetes), and CI/CD pipelines.
• Working knowledge of AWS cloud services and Databricks/Unity Catalog or equivalent enterprise data platforms.
• Excellent verbal and written communication skills.
• Experience working in Agile/Scrum environments.

Education

Bachelor's or Master's degree in Computer Science, Computer Applications, or a related technical field.

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