MLOps Engineer

MLOps Engineer (6-10 years experience) to sit at the intersection of Data Science and Enterprise Operations — architecting, deploying, and optimizing production-grade machine learning pipelines that drive measurable improvements in our organization's MLOps maturity. This is a high-impact, technically demanding role that blends hands-on ML model development with advanced cloud infrastructure orchestration, automation engineering, and cross-functional enablement. Core Technology Stack Category Tools & Platforms ML Platforms GCP Vertex AI AWS SageMaker Azure ML Studio Orchestration Apache Airflow Kubeflow Containerization & IaC Docker Kubernetes Terraform Languages Python Bash AI/LLM Ecosystem LangChain RAG Frameworks Vector Databases Roles & Responsibilities Pipeline Architecture & Automation Design, build, and manage end-to-end automated ML pipelines — encompassing model training, deployment, batch and real-time inference, continuous monitoring, and automated retraining workflows Architect scalable, distributed MLOps infrastructures optimized for high-performance training and inference at enterprise scale MLOps Maturity & Strategy Own and drive the MLOps initiative backlog, systematically advancing the automation maturity, security posture, and operational resilience Proactively introduce modern DevOps principles — CI/CD, GitOps, infrastructure-as-code — tailored to the unique demands of the Data Science lifecycle Model Monitoring & Reliability Establish proactive drift detection frameworks — monitoring both data drift and concept/model drift — to ensure sustained model accuracy and reliability in production environments Define and implement model governance standards, ensuring reproducibility, auditability, and compliance across all ML workloads API Development & Enterprise Integration Develop, test, and publish secure, high-performance REST APIs that enable seamless, production-ready integration of ML models with enterprise business applications Ensure all API implementations meet enterprise standards for security, scalability, and observability Required Experience & Qualifications Proven track record operationalizing production-grade Data Science projects from experimentation through to live deployment Extensive Kubernetes expertise — including advanced cluster management, scaling strategies, and workload optimization Mastery of industry-standard MLOps frameworks with a strong conceptual understanding of ML/AI architectures and hands-on model development experience Deep Python proficiency for both machine learning development and infrastructure automation tasks Strong cloud fluency across one or more major ecosystems: AWS, Microsoft Azure, or Google Cloud Platform (GCP) Hands-on experience with containerization (Docker), Infrastructure-as-Code (IaC), and scalable distributed system design Demonstrated experience building AI agents, Retrieval-Augmented Generation (RAG) systems, enterprise-grade AI platforms, or complex workflow automation solutions Added Advantage: Candidates with the following will have a distinct edge: Hands-on experience with model quantization techniques (e.g., INT8/FP16 conversion) and hardware-level optimization for both edge and cloud deployment environments Deep understanding of model evaluation frameworks — including precision, recall, F1/F2-score trade-offs, AUC-ROC analysis, and production metric alignment with business objectives