Platform Engineer

About us: At Dabster, we are your one-stop destination for talent acquisition and digital innovation. Our customized, scalable talent solutions empower organizations to concentrate on their core business while we expertly match the right talent to the right roles. Who will you work with: Partnering with a global technology leader that is driving innovation across cloud, data, AI, and enterprise solutions. They offer an exciting environment for professionals who want to contribute to impactful digital transformation projects and work on cutting-edge technology initiatives. About the Role: We are looking for an experienced DevOps / Platform Engineer to build and evolve our GenAI / ML platform on AWS. You will design, develop, and operate containerized services and automation tooling that power our AI and ML workloads, enabling scalable, secure, and production‑ready infrastructure for model training, deployment, and monitoring. This role is ideal for engineers with strong Python skills, deep AWS experience, and a passion for GenAI, MLOps, and modern backend/API engineering. Key Responsibilities: Build and maintain GenAI / ML platform infrastructure on AWS using core services such as S3, compute, and deployment platforms (ECS, EC2, Lambda). Design and develop containerised services using Docker, ECS, and APIs (e.g. FastAPI) that are scalable, secure, and production‑ready. Write high‑quality Python code for platform tooling, automation, APIs, and integration with ML workflows. Implement and maintain CI/CD pipelines using Git / GitLab CI and related tooling to automate builds, tests, and deployments. Support ML and GenAI workloads by ensuring reliable infrastructure for model training, deployment, and monitoring. Collaborate with ML engineers and data scientists to understand platform needs and translate them into robust infrastructure and services. Monitor, troubleshoot, and optimise platform performance, reliability, and cost‑efficiency. Explore and evaluate emerging GenAI tools and architectures (e.g. agents, new models) and contribute to R&D and experimentation. Must‑Have Skills: Strong DevOps / Platform Engineer background, with experience building and operating production systems on the cloud. Excellent Python software engineering skills, including automation, tooling, and API development (FastAPI or similar frameworks). Deep expertise in containerised service engineering : Docker AWS ECS (Elastic Container Service) Building scalable backend services and APIs (e.g. FastAPI). Strong proficiency with core AWS cloud services used to build GenAI / ML platforms, including: S3 (storage) Compute and deployment services (ECS, EC2, Lambda). Experience with Git and CI/CD tools such as GitLab CI, and a solid understanding of automation and release processes. Desired Skills (Nice to Have): Experience with AI / MLOps workflows and tooling (model training, deployment, monitoring, pipelines). Familiarity with AWS Bedrock or similar services for accessing foundation models. Exposure to GenAI use cases, LLMs, and agent‑based architectures. Ability to explore emerging GenAI tools. Evaluate new architectures and approaches. Experiment with new ideas and share insights with the team. Understanding of ML model deployment, monitoring, and scaling concepts in production. Familiarity with Kubernetes or other container orchestration platforms. Experience in a banking / financial services or regulated environment. How to apply: If your expertise meets the above job, we would love to hear back from you, kindly share your resume to [email protected]