Machine Learning Engineer

Machine Learning Engineer

What You Will Do: Key Responsibilities

• Design and build robust ML pipelines and services

  for efficient model training, validation, and seamless deployment into production.

• Collaborate closely with data scientists, solution architects, DevOps engineers

  , and other stakeholders to align components and pipelines with overarching project goals and requirements. Communicate any deviations from target architecture effectively.

• Ensure seamless cloud integration

  with

  AWS and Azure cloud services

  for enhanced performance, scalability, and resource utilization.

• Build reusable infrastructure components

  applying best practices from both

  DevOps and MLOps

  .

• Adhere to stringent security standards and regulatory compliance

  , particularly when handling confidential and sensitive data within ML systems.

• Design optimal network plans

  for given Cloud Infrastructure, ensuring compliance with established network security guidelines.

• Monitor model performance in production

  and implement automated drift detection and retraining pipelines to maintain model accuracy and relevance.

• Optimize models for performance, scalability, and cost efficiency

  through techniques such as batching, quantization, and hardware acceleration.

• Create detailed documentation and guidelines

  for the effective use and modification of developed components, fostering knowledge sharing within the team.

Required Qualifications

• Strong

  programming skills in Python

  .
• Deep experience with major

  ML frameworks

  such as TensorFlow, PyTorch, Scikit-learn, and XGBoost.
• Hands-on experience with

  MLOps tools

  like MLflow, Airflow, TFX, Kubeflow, or BentoML.
• Proven experience deploying models using

  Docker and Kubernetes

  .
• Strong knowledge of

  cloud platforms (AWS/GCP/Azure)

  and their respective

  ML services

  (e.g., SageMaker, Vertex AI).
• Proficiency with

  data engineering tools

  including Spark, Kafka, and both SQL/NoSQL databases.
• Solid understanding of

  CI/CD (Continuous Integration/Continuous Delivery)

  principles,

  version control (Git)

  , and

  infrastructure as code (Terraform, Helm)

  .
• Experience with

  monitoring and logging tools

  such as Prometheus, Grafana, and ELK stack.

Good-to-Have Skills

• Experience with

  feature stores

  (e.g., Feast, Tecton) and experiment tracking platforms.
• Knowledge of

  edge/embedded ML, model quantization, and optimization techniques

  .
• Familiarity with

  model governance, security, and compliance

  considerations in ML systems.
• Exposure to

  on-device ML or streaming ML use cases

  .
• Experience leading

  cross-functional initiatives

  or

  mentoring junior engineers

  .