In this role, you will partner closely with ML engineers, researchers, data teams, and platform teams to design scalable infrastructure, automate deployment workflows, and establish engineering standards that ensure reliability, observability, and reproducibility across the machine learning lifecycle.

- Design and implement end-to-end machine learning pipelines covering data ingestion, training, evaluation, deployment, monitoring, and retraining.
- Develop scalable infrastructure that enables consistent and repeatable movement of models from research to production.
- Own model-serving architectures for both batch and real-time inference workloads.
- Establish CI/CD practices for machine learning, including automated testing, model packaging, version control, and deployment automation.
- Build and maintain containerized and orchestrated environments using technologies such as Docker and Kubernetes.
- Optimize infrastructure utilization for compute and GPU-intensive workloads while balancing performance and cost efficiency.
- Implement model and data versioning, reproducibility standards, and rollback mechanisms.
- Develop monitoring, alerting, and observability frameworks for production ML systems.
- Implement mechanisms for detecting data drift, model degradation, latency issues, and operational risks.
- Support continuous feedback loops, human-in-the-loop workflows, and retraining processes that improve model quality over time.
- Translate complex operational challenges into scalable, secure, and maintainable platform solutions.
- Evaluate emerging MLOps technologies, orchestration frameworks, and industry best practices to guide tooling decisions.

Who are you

You bring a strong combination of machine learning infrastructure expertise, software engineering excellence, and a passion for building reliable production systems.

You have a Masters degree or Ph.D. in Computer Science, Artificial Intelligence, Machine Learning, Mathematics, or a related field,



along with demonstrated experience delivering machine learning systems into production environments at scale.

- 4+ years of experience in MLOps, ML platform engineering, or ML infrastructure engineering.
- Deep expertise across the machine learning lifecycle, including training, evaluation, deployment, monitoring, and retraining.
- Strong experience implementing CI/CD pipelines, automated testing, model packaging, and release management for ML systems.
- Hands-on proficiency with Docker, Kubernetes, and cloud platforms such as AWS, Azure, or GCP, including infrastructure-as-code practices.
- Experience building scalable model-serving solutions supporting both batch and real-time inference workloads.
- Solid knowledge of observability, monitoring, data drift detection, model validation, and operational excellence.
- Proficiency in Python and strong software engineering fundamentals, including testing, code quality, and version-control.
- Experience with workflow orchestration and MLOps platforms such as MLflow, Kubeflow, Airflow, DVC, or comparable technologies.
- Practical experience supporting large-scale data processing environments, distributed computing, streaming architectures, or Spark-based systems.
- Familiarity with LLMOps practices, retrieval infrastructure, vector databases, and operationalization of AI-powered systems.
- The ability to make informed architectural decisions, lead technical initiatives, and collaborate effectively across multidisciplinary teams.
- Excellent communication, mentoring, and stakeholder engagement skills.

Exposure to geospatial platforms, spatial data infrastructure, edge AI, TinyML, LiDAR, drone data processing, Go, Java, or C++ is valuable in helping accelerate impact within HERE's innovation ecosystem.

Disclaimer: This job posting has been aggregated from external source. Role details, content, and availability are subject to change. Applicants are advised to confirm the latest information directly on the company website before applying.

📌 Sr ML & AI Engineer (Maharashtra)
🏢 HERE Technologies
📍 Maharashtra