Principal ML Engineer

• Deploy and manage AI models at scale.
• Monitor model performance, hallucination rates, drift, latency, and infrastructure costs.
• Design distributed, event-driven microservices using Python, Go, or TypeScript.
• Build infrastructure-as-code and CI/CD pipelines to ship services.
• Implement agent permission structures, human-in-the-loop workflows, data isolation boundaries, and prompt injection defenses.
• Partner with Product Managers from inception to translate business requirements into scalable architectures.
• Present architectural trade-offs to executives and key client stakeholders.
• Shape next-generation AI platforms for an international client.
• Serve as a trusted technical authority across production systems, applied AI, and engineering excellence.

• 12–15+ years in software engineering, with a clear evolution from Backend/Distributed Systems Architecture into applied Production ML Engineering.
• Deep experience with MLOps, model evaluation rubrics, advanced RAG, vector search (embeddings, HNSW, hybrid search), and fine-tuning.
• Strong mastery of distributed systems, microservices, and asynchronous event-driven patterns in Python, Go, or TypeScript.
• Hands-on command of Docker, cloud infrastructure (AWS/GCP/Azure), and automated CI/CD pipelines.
• Practical knowledge of LLM safety, threat modeling, data boundary enforcement, and agent security.
• Fluent English.
• Ability to articulate complex technical trade-offs clearly to client executives and non-technical stakeholders.
• Prior experience with multi-agent orchestration frameworks (e.g., LlamaIndex, Semantic Kernel, AutoGen, CrewAI, MCP) is nice-to-have.
• Experience in fast-paced consulting, advisory, or high-growth tech platforms is nice-to-have.