Description

We are seeking a seasoned AI Platform Engineer Lead with over 14 years of experience in designing, building, and scaling enterprise-grade AI/ML platforms. The ideal candidate will be responsible for leading technical strategy, architecting robust AI infrastructure, and mentoring engineering teams to deliver cutting-edge AI solutions that drive business transformation. 

Technology Stack/ Key Skill sets :

• Strong hands-on experience with AWS infrastructure (VPC, IAM, KMS, Private Link, Transit Gateway, S3, Lambda, ECS/EKS)
• Hands-on with Snowflake administration — RBAC, network policies, masking, row access policies, warehouses, and resource monitors
• Proficiency with IaC (Terraform/CloudFormation), CI/CD (CodePipeline, GitHub Actions), and configuration management
• Deep understanding of security, networking, encryption, and identity federation

Domain Expertise:

• Proven experience delivering AI/GenAI use cases for financial services

Key Role Expectations: Implement the AWS and per the approved architecture and security design Build and maintain landing zones, accounts, networking, IAM, and Snowflake account/role structures Operate as the on-the-ground infrastructure lead, coordinating closely with customer security and cloud teams Enable AI Engineers with reusable platform components (Bedrock setup, Cortex enablement, secure data zones) Drive infrastructure code reviews, environment promotions, and platform governance Platform Architecture & Development Design and implement scalable AI/ML platforms supporting model training, deployment, monitoring, and lifecycle management. Drive adoption of cloud-native and hybrid architectures for AI workloads. Ensure platform reliability, performance, and security at scale. Leadership & Strategy Lead cross-functional engineering teams, providing technical guidance and mentorship. Collaborate with product, data science, and business stakeholders to align AI platform capabilities with organizational goals. Define long-term AI infrastructure strategy and roadmap. Operational Excellence Establish best practices for MLOps, CI/CD pipelines, and automated workflows. Implement monitoring, observability, and governance frameworks for AI models. Optimize cost efficiency and resource utilization across cloud and on-prem environments. Innovation & Research Stay ahead of emerging AI/ML technologies, frameworks, and tools. Evaluate and integrate new solutions to enhance platform capabilities. Champion innovation by fostering a culture of experimentation and continuous improvement.

Required Qualifications

• 14+ years of experience in software engineering, with at least 5+ years focused on AI/ML platforms.
• Proven expertise in cloud platforms (Azure, AWS, Google Cloud Platform) and container orchestration (Kubernetes, Docker).
• Strong knowledge of MLOps frameworks (Kubeflow, MLflow, SageMaker, Vertex AI).
• Hands-on experience with distributed systems , data pipelines , and model deployment at scale .
• Proficiency in programming languages such as Python, Java, or Go .
• Deep understanding of AI/ML lifecycle management , including monitoring, retraining, and governance.
• Excellent leadership, communication, and stakeholder management skills.

Preferred Qualifications

• Experience in building Enterprise AI platforms for large-scale organizations.
• Familiarity with data engineering tools (Spark, Kafka, Databricks).
• Exposure to generative AI and LLM deployment frameworks .
• Track record of leading global teams and delivering complex AI initiatives

Key Skills

Education

Any Graduate