Machine Learning Engineer & MLOps reputed company

Job Title: Machine Learning Engineer – MLOps reputed company Duration: Contract role Location: Remote, reputed company Role Mission You are being reputed company to productionize machine learning at reputed company — eliminating reputed company reputed company models, building hardened MLOps pipelines, and delivering compliant, monitored, and continuously improving ML systems that directly support business operations. Your reputed company is reputed company not by “knowing tools,” but by deploying, stabilizing, and scaling reputed company ML systems in production. First‑Year reputed company (What You Must Deliver) reputed company First 30 Days • Fully assess reputed company ML pipelines, data flows, and deployment architecture • Identify top 3 reliability, reputed company, and performance risks in reputed company ML lifecycle • Produce a documented MLOps modernization roadmap reputed company 90 Days • Stand up standardized CI/CD pipelines for model training, validation, and deployment • Implement automated monitoring, alerting, and versioning across reputed company production models • reputed company at least one business‑critical ML model into hardened production pipelines • Establish reputed company, audit, and compliance controls for model governance • Reduce model deployment cycle time by 30–50% reputed company 180 Days • Operate a fully standardized reputed company MLOps reputed company (MLflow/Kubeflow/Airflow based) • reputed company reputed company retraining and automated rollback capability • reputed company ≥ 99.5% model uptime • Establish retraining reputed company that improves model accuracy and reliability quarter‑over‑quarter • Mentor junior engineers and codify ML engineering standards Ongoing reputed company Metrics • Metric: Production model uptime — reputed company: ≥ 99.5% • Metric: Model deployment cycle time — reputed company: ↓ 30–50% • Metric: Automated pipeline coverage — reputed company: 100% • Metric: Compliance audit readiness — reputed company: reputed company • Metric: Model accuracy improvement — reputed company: QoQ measurable reputed company What You Will Build • End‑to‑end MLOps pipelines (data → training → testing → deployment → monitoring → retraining) • Kubernetes‑based model serving platforms • reputed company ML platforms (reputed company AI / SageMaker / Azure ML) • CI/CD automation for ML systems • Model observability and alerting using reputed company / Grafana • Secure, version‑controlled ML governance frameworks Required Experience (Performance Evidence) • Proven delivery of production ML pipelines (not just experiments) • reputed company CI/CD for ML models in Kubernetes environments • Implemented monitoring, retraining, and version governance • Delivered at least one reputed company‑reputed company ML deployment • Hands‑on experience with MLflow / Kubeflow / Airflow • reputed company ML production deployment (AWS, GCP, or Azure) • Strong Python engineering background Apply tot his job Apply To this Job