MindWise Technologies
Website:
mindwisetech.com
Job details:
Senior MLOps Engineer
We are seeking an experienced Senior MLOps Engineer to build, deploy, and maintain secure, scalable, and reliable machine learning infrastructure in production. In this role, you will work closely with Data Science, Backend Engineering, and Product teams—as well as directly with client stakeholders—to operationalize machine learning models and establish robust ML engineering and deployment practices.
The ideal candidate will have strong hands-on experience across the end-to-end ML lifecycle, with particular expertise in MLflow, model deployment, ML pipelines, CI/CD, containerization, AWS infrastructure, model monitoring, and production reliability.
Key Responsibilities
- ML Model Deployment & Infrastructure: Design, deploy, containerize, and scale machine learning models and inference services in production using Docker and AWS services such as ECS, EKS, Lambda, and SageMaker.
- MLflow & Model Lifecycle Management: Build and maintain MLflow-based workflows for experiment tracking, model versioning, model registry, model packaging, artifact management, and promotion of models across development, staging, and production environments.
- ML Pipelines & Automation: Build and maintain automated ML pipelines covering data preparation, model training, validation, experiment tracking, model registration, deployment, and retraining.
- CI/CD & MLOps Automation: Develop CI/CD workflows that integrate MLflow with model training and deployment pipelines. Implement automated testing, model validation, artifact management, deployment strategies, and reliable rollback mechanisms.
- Cloud Infrastructure: Design and manage production ML infrastructure on AWS, including compute, networking, storage, IAM, monitoring, and other services required to support machine learning workloads.
- Security & Access Control: Implement security best practices including least-privilege IAM, secure secrets management, network isolation, encryption, role-based access control, and appropriate controls for sensitive data and production ML systems.
- Model Monitoring & Observability: Implement comprehensive monitoring, logging, metrics, and alerting for production ML systems. Monitor model performance, inference latency, resource utilization, data quality, data drift, model drift, and system reliability.
- Data & Model Reproducibility: Establish robust practices for tracking datasets, MLflow experiments, model versions, parameters, metrics, configurations, dependencies, and deployment artifacts to ensure reproducibility and traceability.
- Production Reliability: Ensure ML services meet production requirements for availability, scalability, performance, fault tolerance, and disaster recovery.
- Performance & Cost Optimization: Identify opportunities to optimize model inference, compute resources, infrastructure architecture, and AWS costs while maintaining production performance and reliability.
- Client Communication & Technical Ownership: Serve as a trusted technical voice with clients, communicating infrastructure decisions, deployment architecture, security practices, operational risks, and technical recommendations clearly and professionally.
Required Skills & Experience
- Experience: 4+ years of experience in MLOps, ML Engineering, DevOps, or a closely related field, with hands-on experience deploying and operating machine learning systems in production.
- MLflow: Strong hands-on experience with MLflow, including Experiment Tracking, MLflow Projects, Model Registry, model versioning, artifact management, model packaging, and deployment workflows. Candidates should have used MLflow as part of a production or near-production MLOps environment.
- Machine Learning Lifecycle: Strong understanding of the end-to-end machine learning lifecycle, including data preparation, model training, validation, experiment tracking, model registration, deployment, monitoring, retraining, and model lifecycle management.
- Python & SQL: Strong proficiency in Python and SQL, with experience building production-grade services, automation scripts, and ML pipelines.
- Cloud Infrastructure (AWS): Deep hands-on experience with AWS services such as IAM, VPC, S3, ECS/EKS, Lambda, CloudWatch, and preferably SageMaker.
- Containerization & Orchestration: Strong experience with Docker and Kubernetes/EKS for deploying and managing production ML workloads.
- CI/CD: Strong experience with CI/CD tools such as GitHub Actions, GitLab CI, Jenkins, or equivalent, preferably with experience integrating MLflow into CI/CD workflows.
- ML Pipelines & Workflow Orchestration: Experience building automated ML workflows using tools such as Airflow, Kubeflow, Prefect, or similar technologies.
- Model Monitoring & Observability: Experience implementing production monitoring, logging, metrics, and alerting using tools such as CloudWatch, Prometheus, Grafana, Elasticsearch, or equivalent.
- Database & Data Systems: Good understanding of relational databases, data pipelines, data storage, and infrastructure required to support production machine learning workloads.
- Security: Strong understanding of cloud security, IAM, secrets management, network security, access controls, and secure handling of sensitive data.
- Professional Standards: High sense of ownership, strong problem-solving ability, security-first mindset, attention to reliability, and excellent client-facing communication skills.
- Experience with AWS SageMaker or other managed ML platforms.
Good to Have
- Experience with Kubeflow, Airflow, Prefect, or similar ML/workflow orchestration platforms.
- Experience with feature stores and model serving frameworks.
- Experience with data lake technologies such as S3, Glue, Athena, or Spark.
- Experience with GPU infrastructure and distributed model training/inference.
- Experience with Terraform or other Infrastructure-as-Code tools.
- Familiarity with enterprise compliance standards such as SOC 2, HIPAA, and GDPR.
- Experience working directly with US or international enterprise clients.
- Experience in healthcare or other regulated industries.
- Bachelor's or Master's degree in Computer Science, Engineering, Data Science, or a related field. Candidates from IITs, BITS, or other top-tier institutions would be preferred.
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