AI/Machine Learning Engineer

We are seeking a strategic and technically skilled AI/Machine Learning Engineer to join our AI and engineering team. The ideal candidate will design, build, and productionize ML models and services that deliver measurable product and business impact. This role requires strong software engineering practices, experience with end-to-end ML lifecycle, cloud native model deployment, and close collaboration with product, data, and research partners to make performant, reliable, and maintainable ML systems.

Key Responsibilities for AI/Machine Learning Engineer - Model Development & Production

• Design, develop, and validate ML models and pipelines for supervised, unsupervised, and reinforcement learning use cases, from data ingestion through feature engineering, model training, evaluation, and deployment.
• Build and maintain scalable, reproducible training and inference pipelines using cloud services (e.g., AWS, GCP, Azure), container orchestration (Kubernetes), and infrastructure-as-code for repeatable deployments.
• Collaborate with data engineers, analysts, and product managers to define metrics, design experiments, instrument telemetry, and translate business requirements into ML solutions.
• Implement model evaluation, validation, and monitoring frameworks to track performance, data drift, fairness, and robustness; establish automated alerting and remediation processes.
• Optimize model performance and cost through profiling, quantization, model distillation, caching strategies, and scalable serving architectures (e.g., model servers, feature stores, batching).
• Apply best practices for MLOps, including CI/CD for models and data, experiment tracking, model versioning, and reproducibility using tools such as MLflow, TFX, or similar platforms.
• Ensure models meet security, privacy, and compliance requirements by collaborating with security and legal teams on data handling, access controls, and documentation of model behavior.
• Write clean, testable, and well documented code; participate in design reviews, code reviews, and knowledge sharing across the engineering and research teams.
• Identify opportunities to automate manual workflows, reduce technical debt, and improve model lifecycle efficiency and reliability.
• Mentor junior engineers and researchers, contribute to team processes and playbooks, and present technical designs and project progress to stakeholders as needed.

Required Qualifications - ML Engineering Skills & Experience

• Bachelor's degree in Computer Science, Engineering, Mathematics, Statistics, or related field, or equivalent practical experience.
• 3+ years of hands on experience building and deploying ML models or production ML systems in a commercial environment.
• Proficiency in Python and common ML libraries and frameworks (e.g., TensorFlow, PyTorch, scikit learn); experience with model training, debugging, and performance tuning.
• Experience with cloud ML and data services (e.g., SageMaker, Vertex AI, AI Platform), containerization (Docker), and orchestration (Kubernetes).
• Strong software engineering skills, including test driven development, CI/CD, code reviews, and familiarity with version control systems (e.g., Git).
• Solid understanding of statistics, probability, and evaluation metrics for classification, regression, and ranking tasks; experience designing A/B tests and interpreting results.
• Experience implementing model monitoring, observability, and alerting for production systems; familiarity with feature stores, data validation, and lineage is a plus.
• Excellent problem solving, communication, and collaboration skills; ability to translate product and business requirements into scalable technical solutions.

Preferred Qualifications - Tools, Research & Domain Experience

• Experience with MLOps tooling such as MLflow, TFX, Kubeflow, or similar experiment tracking and pipeline orchestration platforms.
• Hands on experience with inference optimization techniques and edge or real time serving frameworks (e.g., ONNX, TensorRT, Triton).
• Familiarity with feature engineering and feature stores, streaming data platforms (e.g., Kafka), and large scale distributed training systems (e.g., Horovod, DeepSpeed).
• Advanced degree in a technical discipline, published research, or prior experience in a fast moving SaaS, AI startup, or research oriented environment is a plus.

Work Environment & Compensation - AI/ML Engineer Salary & Benefits

• Full time position with an onsite work model.
• Competitive salary commensurate with experience and a comprehensive benefits package, including health insurance, retirement plan options, and paid time off.
• Opportunities for professional development, training, conference attendance, and support for certifications; clear paths for career growth within a collaborative and inclusive team environment.
• Culture that values diversity, equity, and inclusion, work life balance, and employee well being.