Azure DevOps + Python

Summary

Build and automate Azure cloud infrastructure for AI/ML workloads using Python, Terraform, and Azure DevOps, focusing on MLOps, LLMOps, and agentic AI solutions.

Wissen Technology is hiring for Azure DevOps + Python

About Wissen Technology:

At Wissen Technology, we deliver niche, custom-built products that solve complex business challenges across industries worldwide. Founded in 2015, our core philosophy is built around a strong product engineering mindset—ensuring every solution is architected and delivered right the first time. Today, Wissen Technology has a global footprint with 2000+ employees across offices in the US, UK, UAE, India, and Australia. Our commitment to excellence translates into delivering 2X impact compared to traditional service providers. How do we achieve this? Through a combination of deep domain knowledge, cutting-edge technology expertise, and a relentless focus on quality. We don’t just meet expectations—we exceed them by ensuring faster time-to-market, reduced rework, and greater alignment with client objectives. We have a proven track record of building mission-critical systems across industries, including financial services, healthcare, retail, manufacturing, and more. Wissen stands apart through its unique delivery models. Our outcome-based projects ensure predictable costs and timelines, while our agile pods provide clients with the flexibility to adapt to their evolving business needs. Wissen leverages its thought leadership and technology prowess to drive superior business outcomes. Our success is powered by top-tier talent. Our mission is clear: to be the partner of choice for building world-class custom products that deliver exceptional impact—the first time, every time.

Job Summary: We are looking for an DevOps Engineer with strong hands-on experience in Azure cloud engineering, Infrastructure as Code (Terraform), and configuration management/automation. The role primarily focuses on building reliable cloud infrastructure, enabling CI/CD automation, improving observability and operational excellence, and driving cost optimization (FinOps). Additionally, the engineer will be responsible for designing and developing agentic AI solutions that streamline and support infrastructure operations, monitoring triage, and FinOps insights, leveraging contemporary AI agent frameworks and Azure AI services.

The role further encompasses architecting and automating foundational infrastructure, deployment pipelines, monitoring systems, and governance mechanisms essential for ML and GenAI workloads utilizing Azure-native services, Infrastructure as Code (IaC), and advanced MLOps practices. Key responsibilities include enabling orchestration frameworks for LLMs, such as LangGraph, and supporting integration with Claude-based skills and plugins from an infrastructure and platform standpoint.

The ideal candidate brings a strong platform and automation mindset, with experience working closely with CCOE’s and Development teams to provide secure, scalable infrastructure automation and agentic solutions.

Experience: 5 - 8 Years
Location: Bangalore
Mode of Work: Hybrid

Key Responsibilities:

Build, deploy, and manage comprehensive MLOps and LLMOps pipelines on Azure.
Design and oversee CI/CD pipelines for machine learning models and large language model workflows utilizing Harness or Azure DevOps.
Streamline the promotion of models, prompts, and agent workflows between environments through automation.
Establish approval gates, implement rollback mechanisms, and facilitate controlled release processes.
Oversee the lifecycle of ML models and LLM-driven workflows, including their training, assessment, deployment, monitoring, and retraining.
Administer Azure Machine Learning workspaces, computing resources, environments, model registries, and endpoints.
Integrate LLM workflows and agent-centric architectures using LangGraph.
Support the incorporation of Claude-based models, skills, and plugins into enterprise-level applications.
Operationalize prompt versioning, orchestration strategies, and agent workflows in live production settings.
Set up and govern Azure ML and Generative AI infrastructure via Terraform as Infrastructure as Code (IaC).
Standardize development, test, and production environments for ML and GenAI workloads.
Automate recurring platform operations and environment setup tasks.
Deploy both real-time and batch inference solutions for machine learning and LLM use cases.
Enable scalable Generative AI services that are reliable, high-performing, and cost-effective.
Apply blue-green or canary deployment techniques where suitable.
Implement monitoring practices to track model accuracy, data drift, prompt drift, and agent behavior.
Ensure thorough logging, alerting systems, and observability for ML and GenAI platforms.
Adhere to enterprise standards concerning security, access controls, and cost management.
Collaborate closely with engineering, platform, and development teams.
Guide teams toward adopting best practices in MLOps and LLMOps.
Contribute to the creation and maintenance of reusable templates, frameworks, and documentation.