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We’re looking for an AI Platform Engineer to build the foundation for an agentic AI platform that connects data, models, and agents and runs reliably at cloud scale. This is a hands‑on engineering role at the intersection of data, infrastructure, and AI—you’ll ship usable platform components, not just infrastructure, and actively use AI tools to accelerate your own work. The goal: safe, observable, and scalable AI automation that teams can adopt quickly.

Responsibilities

• Engineer robust data and cloud foundations to support AI workloads and automation at scale across AWS environments.
• Design and implement agentic AI systems as part of a broader automation stack—developing self‑orchestrating workflows, APIs, and reusable platform components.
• Build automation and observability solutions using Infrastructure-as-Code (Terraform/CDK) and modern monitoring frameworks.
• Integrate data pipelines with intelligent orchestration layers to enable autonomous experimentation and deployment.
• Collaborate across disciplines—partnering with data engineers, AI scientists, and platform specialists to embed AI capabilities into core operations.
• Champion responsible and scalable AI automation, ensuring compliance, reliability, and transparency by design.

Must have

• Strong software engineering background with Python, TypeScript, or similar languages.
• Proven experience building and automating cloud infrastructure using AWS (EC2, IAM, S3, Lambda, CloudWatch, etc.) and Infrastructure-as-Code tools (Terraform/CDK).
• Solid understanding of data engineering principles—pipeline design, data flow orchestration, and API integration.
• Experience with CI/CD pipelines and Git-based workflows.
• Demonstrated experience in use of AI tools (e.g., Claude, Copilot, LangChain, agent frameworks) to enhance productivity.
• A self‑driven, curious problem solver who experiments and iterates toward impactful solutions.

Nice to have

• Experience deploying or integrating agentic AI architectures, such as orchestration frameworks or autonomous agents.
• Knowledge of RAG pipelines, embeddings, and vector databases (e.g., Weaviate, Qdrant).
• Familiarity with Kubernetes/EKS and observability tools (New Relic, OpenTelemetry).
• Understanding of model hosting and optimization (vLLM, Triton, HuggingFace).
• Awareness of data governance and responsible AI practices in enterprise or regulated settings.

Seniority level

Mid‑Senior level

Employment type

Full‑time

Job function

Information Technology

Industries

IT Services and IT Consulting

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