Senior Machine Learning Engineer - PyTorch/Tensorflow (10-15 yrs)

Key Responsibilities :

Machine Learning Model Development :

- Lead the design, development, and implementation of machine learning models across various domains (such as computer vision, NLP, time-series forecasting, etc.).

- Build end-to-end ML pipelines for model training, evaluation, and deployment in production environments.

- Conduct performance analysis, optimization, and tuning of machine learning models.

- Collaborate with data scientists and domain experts to translate business requirements into data-driven solutions.

MLOps Implementation :

- Drive the implementation and optimization of MLOps pipelines for continuous integration, delivery, and monitoring of ML models in production.

- Automate workflows, build reproducible experiments, and enhance model versioning using tools like MLflow, Kubeflow, or TFX.

- Deploy ML models to production environments, ensuring they are scalable, reliable, and performant.

- Collaborate with the data engineering team to ensure seamless data flows from storage to model ingestion.

DevOps and Infrastructure Management :

- Collaborate with the DevOps team to design and implement scalable, cloud-native architectures for ML workloads using platforms like AWS, GCP, or Azure.

- Automate the setup, deployment, and management of ML environments in the cloud, leveraging tools like Docker, Kubernetes, Terraform, and CI/CD pipelines.

- Manage the infrastructure necessary for scaling machine learning workloads efficiently (e.g., GPU provisioning, distributed computing frameworks).

- Ensure smooth integration of ML models with the existing cloud infrastructure, improving model performance and resource usage.

Collaboration & Mentorship :

- Work closely with cross-functional teams, including data scientists, software engineers, product managers, and business stakeholders to deliver impactful machine learning solutions.

- Provide mentorship and guidance to junior engineers, fostering best practices in MLOps, DevOps, and machine learning engineering.

- Advocate for strong software engineering practices within the ML team to ensure quality, scalability, and maintainability of solutions.

Model Monitoring and Maintenance :

- Implement monitoring solutions to track the performance of deployed ML models and alert on anomalies or drift.

- Lead the effort to continuously evaluate, retrain, and update models as new data becomes available or business requirements evolve.

Qualifications :

Required :

- 10+ years of experience in software engineering, with at least 5 years dedicated to machine learning engineering and 5 years of MLOps/DevOps experience.

- Strong proficiency in Python and machine learning frameworks such as TensorFlow, PyTorch, Scikit-learn, or XGBoost.

- Deep experience with cloud platforms (AWS, GCP, or Azure) and containerization technologies like Docker and Kubernetes.

- Experience with building and maintaining CI/CD pipelines for ML systems, using tools such as Jenkins, GitLab CI, or CircleCI.

- Expertise in managing and optimizing ML models for production environments, including deployment and monitoring.

- Solid understanding of DevOps practices, including infrastructure-as-code, version control, and automated testing.

- Experience with distributed computing tools such as Apache Spark, Dask, or Ray.

- Familiarity with data storage solutions such as SQL, NoSQL, and object storage systems (e.g., S3, BigQuery).

Preferred :

- Experience with advanced MLOps tools like MLflow, Kubeflow, or TensorFlow Extended (TFX).

- Familiarity with model deployment frameworks such as Seldon or TFX Serving.

- Knowledge of model interpretability techniques and tools (e.g., SHAP, LIME).

- Experience in agile development methodologies.

- Advanced degree (Masters or PhD) in Computer Science, Data Science, Machine Learning, or a related field.

Skills and Competencies :

Technical :

- Strong understanding of ML lifecycle management, including data pipelines, model training, testing, validation, deployment, and monitoring.

- Knowledge of cloud infrastructure design, container orchestration, and microservices architecture.

- Proficient in source control systems (e.g., Git) and Agile development processes.

Problem Solving :

- Ability to design solutions for complex, real-world problems with a focus on automation, scalability, and performance.

- Strong troubleshooting skills in distributed systems and ML workflows.

Leadership & Communication :

- Proven experience in leading cross-functional teams and providing mentorship to junior engineers.

- Excellent communication skills, with the ability to convey technical concepts to non-technical stakeholders.