Sr ML Engineer - Remote
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Job description
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
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