Senior AI Platform Engineer; Data and Analytics Cloud Engineer

Senior Engineer/Platform Leader – AI/ML and Generative AI Platforms

Job Type: Regular

Language Fluency: English (Required)

Work Shift: 1st shift (United States of America)

Accountable for designing, building, and operating secure, scalable AI/ML and Generative AI (GenAI) platforms in the cloud. This role develops and maintains reusable platform capabilities so teams can deliver business outcomes faster while meeting technology standards, security requirements, and regulatory obligations.

Key Responsibilities

• Design, build, and execute the AI/ML and GenAI platform strategy aligned to enterprise architecture, security, and risk standards.
• Own the engineering and lifecycle management of AI/ML platform components such as development work spaces, training/inference patterns, model registry, feature storage patterns, experiment tracking, prompt/version management, retrieval-augmented generation (RAG) enablement, and reusable templates for safe and deliberate consumption across the organization.
• Establish and champion Dev Sec Ops  practices for platform delivery, including Git Lab source control, build automation, and CI/CD pipelines for infrastructure and application deployments.
• Deploy infrastructure as code (IaC) to the cloud using Terraform modules and pipelines; define standards for environments, networking, identity, secrets, encryption, logging, and configuration management.
• Partner with Cybersecurity, Risk, and other 2nd line of defense teams to implement and evidence required security controls (e.g., IAM least privilege, network segmentation, encryption, vulnerability management, audit logging, and policy-as-code) across platform services.
• Implement governance patterns for AI/ML and GenAI (e.g., model and prompt lifecycle controls, lineage/traceability, approvals, change management, risk assessments, and operational readiness) consistent with enterprise data governance and regulatory obligations.
• Provide technical leadership and hands‑on engineering to solve complex platform problems (performance, reliability, scalability, cost, and security) and guide engineers through designs, reviews, and delivery.
• Build platform reliability through automation and observability (monitoring, logging, tracing, SLOs), and partner with production support teams to increase resiliency, reduce toil, and improve time to recover.
• Enable self‑service platform consumption via standardized APIs, reusable pipelines, templates, and documentation; in an Agile environment, may serve as an Agile/Dev Sec Ops  champion to accelerate delivery while maintaining compliance.

Qualifications

Required Qualifications:

• Undergraduate degree in computer science, analytics, data engineering, finance or equivalent.
• At least 3 years of experience driving enterprise data strategy, data execution, data engineering or software delivery.
• Expert problem‑solving skills and ability to define detailed strategies.
• Experience in financial services or payments industry.
• Experience in meeting regulatory obligations and operating in a highly regulatory environment on the cloud.
• Experience building a high performing team.

Preferred Qualifications:

• Master’s degree and/or 8+ years of progressive experience delivering complex cloud platforms, preferably supporting AI/ML or analytics workloads at enterprise scale.
• Experience building AI/ML platforms and/or MLOps capabilities such as training/inference automation, model packaging and deployment, model registry, experiment tracking, and operational monitoring.
• Experience with container platforms and orchestration (e.g., Kubernetes/EKS), API enablement, and modern ML tooling (e.g., Python ML ecosystem).
• Deep expertise in AWS (compute, networking, security/IAM, logging/monitoring, managed services) and moderate experience with Azure services and deployment patterns.
• Hands‑on Dev Ops/Dev Sec Ops  experience building CI/CD pipelines (Git Lab), including automated testing, security scanning, artifact management, and controlled deployments across environments.
• Strong infrastructure‑as‑code experience deploying cloud components using Terraform; ability to build reusable modules and enforce standards/guardrails.
• Relevant cloud and security…