Principal AI​/ML Engineer

Job Description:

Note:

Fidelity will not provide immigration sponsorship for this position. Position

Description:

Designs and develops advanced Machine Learning (ML) and trustworthy AI systems that support large-scale, enterprise-wide platforms. Focuses on secure model development, AI safety, multi-agent orchestration, and cloud-scale ML infrastructure, enabling reliable, transparent, and high-assurance deployment of AI across critical business functions. Builds robust pipelines for model training, inference, and continuous monitoring, ensuring compliance and resilience under real-world conditions. Designs model lifecycle operations (MLOps) infrastructure including Sage Maker and Kubernetes for reproducible, scalable, and secure ML deployment.

Responsible for research and prototyping of cutting-edge AI methodologies, including federated learning, adversarial robustness, and multi-agent safety mechanisms.

Primary Responsibilities:

  Architects and implements AI/ML systems that support model training, inference, observability, and continuous monitoring across distributed cloud environments. Develops secure and trustworthy AI frameworks, including adversarial robustness pipelines, anomaly detection models, and model governance mechanisms that ensure compliance, transparency, and risk mitigation. Builds and optimizes agentic AI workflows that automate data pipelines, model lifecycle operations, and system self-diagnostics. Supports research and prototyping of advanced AI/ML methodologies, including hybrid neural architectures, federated learning, adversarial learning, and multi-agent AI safety mechanisms.

Conducts performance, reliability, and robustness evaluations of AI systems under real-world constraints, high-throughput workloads, and adversarial conditions. Collaborate with cybersecurity, cloud engineering, and data science teams to integrate AI safety and model assurance into enterprise architectures. Collaborates with cross-functional teams to integrate AI safety and governance into enterprise architectures while mentoring engineering teams on advanced ML algorithms and secure development practices.

Delivers technical guidance and mentorship to cross-functional engineering teams on advanced ML algorithms, infrastructure patterns, and secure development practices.

Education and Experience:

Bachelor’s degree in Computer Science, Engineering, Information Technology, Information Systems, or a closely related field (or foreign education equivalent) and five (5) years of experience as a Principal AI/ML Engineer (or closely related occupation) developing ML platform applications for Cloud infrastructures (Amazon Web Services (AWS), Azure, Google, and IBM) using agile methodologies. Or, alternatively, Master’s degree in Computer Science, Engineering, Information Technology, Information Systems, or a closely related field (or foreign education equivalent) and three (3) years of experience as a Principal AI/ML Engineer (or closely related occupation) developing ML platform applications for Cloud infrastructures (Amazon Web Services (AWS), Azure, Google, and IBM) using agile methodologies.

Skills and Knowledge:
Candidate must also possess:
Demonstrated Expertise (“DE”) delivering scalable, secure, and distributed applications with robust Identity and Access Management (IAM) and encryption (Knowledge Management System (KMS)), by architecting and deploying enterprise-scale AI/ML systems and auto-ML infrastructure through Infrastructure as code (IAC) and using Cloud-native platforms (Terraform, AWS, Azure, or GCP) and container orchestration frameworks (Kubeflow). DE architecting and engineering high-performance big data applications (AWS Glue, EMR, Kinesis, Athena, and Dynamo DB) and autonomous…