ML Ops and DevOps Lead

Responsibilities

• Design and deploy scalable AI infrastructure using GitHub Actions, EKS, ECS, and AWS Lambda; automate all resource provisioning via Terraform.
• Create and manage end-to-end CI/CD pipelines for model deployment, versioning, and lifecycle management using Azure DevOps and GitHub Actions.
• Deploy machine learning and large language models via Amazon SageMaker, EKS, and Azure ML Endpoints.
• Implement comprehensive monitoring for model performance and drift using Amazon CloudWatch, SageMaker Model Monitor, and OpenSearch.
• Integrate advanced services including Azure OpenAI, Azure Cognitive Services, and AWS Rekognition into enterprise workflows.
• Design robust data pipelines and real-time ingestion streams using AWS Glue, Amazon Redshift, Athena, and Apache Spark.
• Enforce cloud security through AWS WAF, Azure Key Vault, and RBAC, ensuring all deployments meet GDPR and ISO compliance standards.
• Supervise team activities and contribute to change initiatives in line with the Bank’s continuous improvement standards.
• Supporting, Coaching and Mentoring more junior members of the team, when required
• Additional responsibilities on occasion, on management request

Requirements

• A Bachelor’s degree in Computer Science, AI, Software Engineering, Statistics, Mathematics, or a related quantitative field.
• A minimum of 5 years in designing and deploying AI solutions in cloud environments, specifically within MLOps/LLMOps frameworks.
• Proficiency with GitHub Actions, Spark, Redshift, MongoDB, and Azure Purview.
• Deep hands-on expertise in AWS (SageMaker, EKS, S3) and Azure (OpenAI, Cognitive Services) ecosystems.
• Mastery of Terraform for multi-cloud resource automation.
• Proven experience in both MLOps (standard ML) and LLMOps (Large Language Models), focusing on fine-tuning, orchestration, and monitoring.
• Expertise in managing container images via ECR/ACR and deploying via Kubernetes (EKS/AKS).
• Strong understanding of secure networking (WAF, NSGs) and secret management.

Preferred Qualifications

• Azure and/or AWS certifications (e.g., AWS Certified Machine Learning - Specialty or Azure AI Engineer Associate) are highly preferred.
• Ability to seamlessly manage workloads that span across both AWS and Azure.
• Skill in communicating complex technical infrastructure requirements to non-technical business stakeholders.
• Adaptability in troubleshooting model drift and performance bottlenecks in real-time agentic workflows.
• Proactive identification of new tools (e.g., Vector DBs, orchestration frameworks) to improve deployment velocity.

Benefits

• Competitive salary.
• Visa Sponsorship for the successful individual.
• Comprehensive health insurance for the successful individual.
• Professional development and certification support.
• Opportunity to work on cutting-edge AI projects.
• Career advancement opportunities in a rapidly growing AI company.

About the Company

DeepLight AI is a specialist AI and data consultancy with extensive experience implementing intelligent enterprise systems across multiple industries, with particular depth in financial services and banking. Our team combines deep expertise in data science, statistical modeling, AI/ML technologies, workflow automation, and systems integration with a practical understanding of complex business operations.