Senior AI/ML Engineer

Responsibilities

• Evaluates performance results and recommends major changes affecting short-term project growth and success.
• Functions as a technical expert across multiple project assignments.
• Designs, builds, and maintains AI/ML lifecycle core services and products.
• Create utilities and ensure seamless function of orchestration capabilities to support the AI/ML environment.
• Create infrastructure for training, validating, and deploying machine learning models.
• Create reusable components and libraries to accelerate AI/ML development and use of Generative AI capabilities.
• Build model serving platforms for efficient inference.
• Implement automated machine learning pipelines and MLOps practices for continuous integration and deployment (CI/CD) of models.
• Optimize AI/ML algorithms for performance and scalability.
• Troubleshoot complex issues in AI/ML systems.

Requirements

• Bachelor's degree in Computer Science, Statistics, Engineering or other related discipline.
• 8+ years of experience.
• 3+ years of software engineering experience with deep proficiency in Python.
• Proven experience operationalizing Machine Learning models in production environments.
• Hands-on experience with model evaluation, versioning, and CI/CD integration.
• Strong API development skills.
• Familiarity with MLOps frameworks such as AWS SageMaker, MLflow, Kubeflow, or Airflow.
• Expertise in scalable model serving platforms such as TensorFlow Serving, TorchServe, or ONNX runtime.
• Advanced knowledge of cloud platforms (e.g., AWS, Azure, or GCP) and DevOps practices including Terraform or Helm.

Preferred Qualifications

• 10+ years of experience.
• Hands-on experience with Kubernetes and deploying containerized ML services.
• Experience working with feature stores.
• Experience utilizing Databricks or similar cloud-native ML tooling.
• Experience working with feature stores and distributed data frameworks (e.g., Spark).
• Proficiency in model interpretation tools like SHAP or LIME.
• Foundational understanding of linear algebra, calculus, and numerical computation to support algorithm optimization.