zorba ai
Website:
zorbaconsulting.in
Job details:
A technology consulting firm operating in the IT Services & Data Science consulting sector, delivering enterprise-grade AI, ML, and analytics solutions to clients across finance, retail, and enterprise verticals. We build production ML systems that drive business outcomes, with a strong focus on model reliability, scalability, and MLOps best practices. Role: Senior Machine Learning Engineer (on-site, India) ? responsible for designing, building, and productionizing ML systems for high-impact customer engagements. Role & Responsibilities Design and implement end-to-end ML solutions: data ingestion, feature engineering, model development, validation, and deployment to production. Productionize models with robust MLOps practices: containerization, CI/CD pipelines, model versioning, automated testing, and deployment orchestration. Build scalable inference services and APIs using container orchestration and scalable compute to meet latency and throughput SLAs. Collaborate with data engineers, product managers, and stakeholders to translate business requirements into measurable ML deliverables and deployment roadmaps. Implement monitoring, alerting, and model observability (performance, drift detection, and explainability) to ensure continuous model health in production. Drive code quality through reviews, mentoring junior engineers, and documenting reproducible experiments and engineering playbooks. Skills & Qualifications Must-Have Python PyTorch TensorFlow Scikit-learn Docker Kubernetes Preferred AWS SageMaker MLflow Apache Spark Benefits & Culture Highlights On-site role with direct client engagement and high-visibility projects across industries. Opportunities for technical leadership, mentorship, and contribution to engineering best practices and IP. Competitive compensation, learning budget for advanced ML tooling, and fast career progression for high performers. Location: On-site (India). Candidates should be prepared for client-facing work, hands-on implementation, and delivering production-ready ML assets end-to-end.
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