Senior ML Engineer

SUMMARY:
As a Senior ML Engineer, you will bridge the gap between data science prototypes and production-grade software. You’ll build scalable microservices, optimize open-source LLMs on AKS, and establish automated MLOps and governance pipelines across Databricks and cloud infrastructure.

POSITION INFO:
The Tech Stack Deep-Dive Core Platforms & Infrastructure Databricks (Workflows, Model Serving, MLflow, Mosaic AI) Azure Kubernetes Service (AKS), Docker, Kubernetes Languages & Service Architecture Python, SQL Custom REST APIs & Microservices Must-Haves Production MLOps: Hands-on CI\/CD, automated testing, model monitoring, drift detection, and observability. GenAI Deployment: Proven track record deploying AI Agents, RAG solutions, and LLMs into production. Containerization & Compute: Deep experience running containerized workloads and custom APIs on Kubernetes (AKS). Nice-to-Haves Certifications: Databricks (ML\/Generative AI Engineer), CKA\/CKAD, or Azure AI\/Cloud Certifications (AZ-104, AI-102). Advanced Degree: Master's or Doctorate in Computer Science, Engineering, Mathematical Statistics, or quantitative field. Distributed Computing: Apache Spark and large-scale data processing frameworks. Key Responsibilities (Keep it brief) Architect & Serve AI Engines: Build, containerize, and deploy production-ready ML pipelines, LLMs, and custom REST APIs on Databricks and AKS. Implement Enterprise MLOps: Establish automated CI\/CD workflows, drift detection, and observability frameworks for real-time model reliability. Bridge Data Science & Platform: Partner with Data Scientists to refactor notebooks into scalable microservices and resilient platform services. Set Technical Standards: Mentor junior engineers, optimize solution cost\/performance, and maintain enterprise security and governance compliance. Why Join Us? Production at Scale: Work beyond experimental models by shipping live AI agents, LLMs, and microservices into real-world architectures. Bleeding-Edge Stack: Architect using a modern platform ecosystem combining Databricks Mosaic AI, AKS, and cloud-native MLOps tooling. Engineering Impact: Lead technical standards, influence MLOps culture, and mentor top-tier engineering talent in a fast-moving, outcome-driven environment