AI / ML Platform Engineer Specialist

Role & responsibilities

• Develop, improve, and maintain the MLOps platform to enable scalable, reproducible, and observable machine learning and generative AI workflows.
• Design and operate core ML infrastructure (feature stores, model registries, CI/CD pipelines, and data pipelines) using AWS services such as SageMaker, ECS/EKS, Lambda, and Step Functions.
• Enable and support AI and ML development teams, providing best practices, tooling, and technical guidance on leveraging the platform for training, fine-tuning, and deployment.
• Drive technology and architecture decisions across the ML stack, including frameworks, data processing, orchestration, and monitoring tools.
• Collaborate with AI engineering teams to integrate LLMs and generative AI capabilities into products through standardized, secure, and auditable infrastructure.
• Ensure platform scalability, reliability, and compliance by applying DevOps, infrastructure-as-code (IaC), and observability best practices.
• Continuously evaluate and integrate emerging technologies (e.g., LangChain, Ray, MLflow, Kubeflow, Hugging Face) to enhance developer productivity and operational efficiency.

Preferred candidate profile

• Bachelors or Master’s degree in Computer Science, Software Engineering, or related field.
• 5+ years of experience in ML/AI platform or infrastructure engineering, preferably in enterprise or SaaS environments.
• Strong experience with AWS cloud services (SageMaker, ECS/EKS, S3, CloudFormation/Terraform, Step Functions, Lambda).
• Expertise in MLOps frameworks and tools (MLflow, Kubeflow, Vertex AI, Azure ML, or equivalent).
• Solid software engineering background with proficiency in Python, containerization (Docker), and Kubernetes orchestration.
• Proven ability to design and operate scalable data and ML infrastructure with a focus on automation, observability, and governance.
• Familiarity with vector databases (FAISS, Pinecone, Weaviate) and LLM infrastructure (RAG, prompt orchestration, model serving).
• Understanding of security, access control, and compliance in AI/ML environments.