DevOps Engineer

About the Role

We are looking for a highly motivated and versatile AI/ML Enablement Engineer to support and accelerate our organization's AI/ML initiatives. This role sits at the intersection of software engineering, DevOps, and applied AI, with a focus on building robust, scalable, and secure infrastructure and tools that enable data scientists, machine learning engineers, and AI product teams to deliver value faster.

You will collaborate closely with AI/ML stakeholders across the business, helping integrate AI capabilities into existing systems, streamline model deployment, and ensure operational excellence for AI/ML workloads.

Key Responsibilities:

● By leveraging cloud-native services, design and maintain scalable, secure, and cost-efficient infrastructure to support AI/ML workloads across training, inference, and integration use cases.

● Build reusable tools and templates, including Infrastructure-as-Code (IaC) modules, CI/CD pipelines for model packaging, testing, and deployment, and containerized development environments with Docker and Kubernetes to provision AI-ready infrastructure.

● Enable integration of AI services (e.g., OpenAI, Anthropic, Hugging Face) into applications and internal platforms by assisting development teams in securely leveraging APIs, SDKs, or backend services.

● Collaborate with application architects to embed AI functionality such as summarization, chat, or classification; supporting centralized integration patterns for prompt templates, key management, rate-limiting, caching, and observability; and advising internal teams on service capabilities, limitations, and cost considerations as the landscape evolves.

● Support ML engineers and developers by collaborating to ensure access to reliable, secure environments for experimentation and delivery; providing deployment support and standardized templates for serving models in both batch and real-time settings.

● Implementing CLI tools that enable teams to safely create AI environments within defined guardrails; integrating access policies and usage limits, configuring autoscaling, usage monitoring, and alerting to optimize utilization of high-cost resources like GPUs and inference APIs.

● Contribute to codebases (Python, Golang preferred) for platform tools, SDKs, APIs, or internal utilities that enable AI adoption.

● Establish best practices for CI/CD, observability, testing, and security in AI/ML development environments.

● Enable cross-functional teams by developing documentation, templates, and reusable assets to support AI/ML use cases across the company.

Required Qualifications:

● 5+ years of experience in DevOps, Site Reliability, or Platform Engineering roles.

● Proficiency in Python or Golang (for scripting, automation, or tools).

● Hands-on experience with cloud infrastructure (Azure, AWS, or GCP), particularly in secure, enterprise environments.

● Strong knowledge of Infrastructure as Code tools (e.g., Terraform, Bicep, Pulumi).

● Experience with CI/CD pipelines, GitOps, and container technologies (e.g., Docker, Kubernetes).

● Familiarity with monitoring/logging tools (e.g., Prometheus, Grafana, Azure Monitor).

● Understanding of basic AI/ML service usage (e.g., calling AI APIs, integrating pre-trained models, prompt engineering).

● Comfortable supporting cross-functional teams, especially developers and data scientists.