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We are looking for a senior-level AI/ML professional to lead the productionization, deployment, and continuous optimization of ML systems in mission-critical environments. This role requires strong technical leadership, deep hands-on experience shipping models to production, and the ability to drive reliability, scalability, and cost/performance trade-offs end-to-end. The ideal candidate combines strong ML depth with MLOps and production engineering excellence, and can operate autonomously while influencing architecture and best practices across teams. Key Qualifications Master’s, PhD, or equivalent experience in Computer Science, Engineering, Data Science (or related), with strong foundations in mathematics, statistics, and ML. 6+ years (or equivalent) experience across ML engineering and MLOps /production ML, with a proven track record delivering high-impact systems in production. Demonstrated expertise deploying models to production: scalable serving patterns, model packaging, inference optimization, observability, and safe rollout strategies. Strong experience with model performance and efficiency optimization: latency/throughput tuning, batching, quantization, ONNX/TensorRT, GPU/CPU optimization, memory and cost tuning. ● Strong proficiency in Python. Hands-on experience with ML frameworks such as PyTorch, scikit-learn, and optimization/runtime stacks like ONNX, TensorRT. Strong grasp of CI/CD for ML (e.g., GitLab CI), containerization (Docker), and reproducible workflows. Experience with monitoring and reliability: metrics/logging/tracing, model/data drift, alerting, SLOs, incident response practices. Kubernetes, AWS, FastAPI (and/or similar Python serving frameworks), SageMaker, service meshes, feature stores, model registries. Key Responsibilities Handle the production lifecycle of ML systems: training, packaging, deployment, monitoring and controlled rollouts. Design, build, and continuously improve training and retraining pipelines (data ingestion, labeling/QA loops, feature generation, experiment tracking, reproducibility). Build and improve high-performance inference services and deployment patterns. Drive inference optimization (ONNX/TensorRT, quantization, batching, GPU/CPU tuning) with measurable latency/cost improvements. Partner with DevOps/SRE to ensure operational excellence: scaling, reliability, incident readiness, observability, and alerting. Own model versioning, CI/CD, and release workflows ensuring reproducibility and safe delivery to production. Strengthen data & model monitoring (performance, drift, quality) and implement feedback loops for continuous improvement. Communicate results and trade-offs to stakeholders, turning technical outcomes into business value. Ensure model quality through rigorous evaluation and production-grade validation aligned with evolving data and business needs. Originally posted on Himalayas