About Infinite Electronics:

Infinite Electronics is a global manufacturer of high-performance connectivity solutions, serving customers across a wide range of industries. With deep engineering expertise and a focus on precision-built components and assemblies, the company partners closely with customers to address complex, real-world challenges and accelerate product innovation.

About Infinite India (GCC):

Infinite is establishing its India Global Capability Center (GCC) in Pune to expand its engineering, digital, and customer service capabilities. This center will play an important role in supporting mission-critical initiatives while working in close collaboration with global stakeholders.
This is an opportunity to be part of the early team shaping how the center operates — influencing technology standards, scalable processes, and collaborative ways of working from the outset. Located at Mont Clare, Baner, the center provides a modern work environment designed to foster collaboration, innovation, and long-term career growth.
This is a unique opportunity to grow alongside a center that is being built with long-term capability and excellence in mind.

Why Join:

Build from the start – Be part of the early team shaping foundational systems, standards, and ways of working
Global exposure – Collaborate directly with international stakeholders on impactful engineering and digital initiatives.
Modern environment, long-term growth – Work from a state-of-the-art office in Baner, Pune, and grow your career within a center designed for sustained capability expansion.
Built with intent – The India GCC is being developed with a strong focus on capability, ownership, and long-term excellence

Position Name: Azure DevOps Engineer

Location: Pune, India

Exempt / Non-Exempt: Exempt

Reports To: Sr. Manager of Software Development Engineering, US

Position Description:

The DevOps Engineer is a hands-on cloud platform and infrastructure engineer based in Pune, reporting to U.S. engineering leadership and working within a small AI engineering team. This is a delivery-focused role - the primary accountability is designing, operating, and securing cloud infrastructure, CI/CD pipelines, and MLOps platform that support enterprise applications, data pipelines, and AI/GenAI workloads in production.

This role owns platform operations end-to-end - from environment provisioning and deployment automation through monitoring, incident response, and continuous improvement - including the deployment, operation, and lifecycle management of AI models and agents on the production AI platform. That scope is delivered in close partnership with U.S. engineering, application, data, AI, and security teams, with a consistent focus on reliability, security, compliance, and delivery speed.

The right candidate is equally comfortable writing infrastructure-as-code and debugging infrastructure failures, building CI/CD pipelines for application code and AI/ML artifacts, and implementing observability for distributed systems and production AI workloads.

Work Location & Schedule Expectations:

This role is based in Pune and works in close daily collaboration with U.S.-based engineering leadership and cross-functional teams.

Work Model:

Hybrid, minimum 3 days per week in the office, coordinated with the team to ensure consistent shared in-person working days.

U.S. Collaboration:

Daily schedule must include 3 to 4 hours of overlap with U.S. Eastern Time (ET) to support active collaboration with U.S. engineering, product, and platform teams.

Operational Availability:

As a hands-on engineer accountable for cloud infrastructure and production platform services, this role requires availability during critical releases, deployments, and production incidents, which may occasionally fall outside standard working hours including early mornings, evenings, or weekends.

Qualifications & Experience:

Required Experience:

Bachelor's degree in computer science, Engineering, or a related technical field, or equivalent practical experience.
Strong track record designing, building, and operating production-grade cloud infrastructure and platform services, with clear ownership of reliability, security, and operational outcomes. Hands-on Azure experience is preferred, though candidates with equivalent depth on AWS or GCP are encouraged to apply.
Demonstrated experience with cloud compute, networking, identity and access management, and storage services, with a focus on secure, scalable, multi-environment architectures across Dev, Stage, and Production.
Expertise in CI/CD pipeline design and automation supporting application code, infrastructure-as-code, data pipelines, and AI/ML artifacts, with working knowledge of deployment strategies such as blue/green and canary releases.
Hands-on experience with infrastructure-as-code tooling such as Bicep or Terraform for environment provisioning and configuration management.
Proven experience implementing monitoring, logging, tracing, and alerting for distributed systems, with demonstrated ownership of SLA/SLOs, incident management, root cause analysis, and production stabilization.
Strong understanding of cloud security, access control, and compliance, including RBAC, secrets management, network isolation, and audit requirements.
Ships infrastructure and platform improvements incrementally, communicates tradeoffs and operational status clearly across technical and non-technical stakeholders, and treats platform reliability and security as production responsibilities rather than implementation tasks.
Comfortable using AI coding assistants as part of a standard development workflow, with the judgment to validate, test, and take ownership of AI-generated code and configuration in production contexts.

Preferred Experience:

Master's degree in computer science, Engineering, or a related field.
Experience supporting deployment, operation, and lifecycle management of AI models and agents in production, including model versioning, deployment pipelines, runtime monitoring, drift detection, and lifecycle management across MLOps platforms such as Azure AI Foundry, Azure Machine Learning, or equivalent AWS or GCP services.
Experience implementing observability for AI systems, including inference latency, throughput, error rates, and drift indicators.
Experience with containerization and orchestration using Docker, Kubernetes, or managed Kubernetes services such as AKS, EKS, or GKE.
Familiarity with cost optimization strategies and performance tuning across cloud infrastructure and workloads.
Experience with end-to-end CI/CD across code, data, and model artifacts, ensuring reproducibility and version alignment across environments.
Familiarity with responsible AI, governance, and compliance frameworks related to AI systems in enterprise environments.
Hands-on experience within the Microsoft Azure ecosystem, including services such as App Service, Functions, Service Bus, Blob Storage, Key Vault, Application Insights, and AI Foundry, with familiarity with Bicep for infrastructure as code. Candidates with equivalent depth on AWS or GCP are encouraged to apply.

Key Duties and Responsibilities:

Cloud Platform & Infrastructure Engineering:

Design, provision, and operate secure, scalable cloud environments across Dev, Stage, and Production using infrastructure-as-code, ensuring strong environment isolation, networking controls, and identity-based access management.
Enforce platform security, compliance, and governance standards, including secrets management, RBAC, audit trails, and network isolation across all environments.
Manage cloud cost optimization and performance tuning across compute, storage, and networking layers, identifying inefficiencies and implementing improvements incrementally.

CI/CD & Automation:

Build and maintain end-to-end CI/CD pipelines for application code, infrastructure, data pipelines, and AI/ML artifacts, enabling reproducible, version-aligned releases across environments.
Implement automated testing, deployment strategies such as blue/green and canary releases, and rollback mechanisms to reduce release risk and improve delivery quality.
Drive automation and standardization across environments to improve delivery speed, consistency, and operational efficiency.

Reliability, Observability & Operations:

Implement and maintain monitoring, logging, tracing, and alerting across distributed systems to ensure high availability and rapid incident response.
Define and operate against SLA/SLOs, leading incident response, root cause analysis, and systemic improvements that reduce recurrence.
Support production readiness, go-live, and stabilization for new services and platform changes, ensuring long-term reliability and performance.

AI Platform Enablement & MLOps:

Enable deployment, scaling, and lifecycle management of AI models and agents on the production AI platform, including CI/CD and MLOps workflows that ensure reproducible and version-controlled model releases.
Implement observability for AI systems, including inference latency, throughput, error rates, and usage patterns, ensuring operational visibility and early detection of degradation or drift.
Enforce runtime governance, security, and compliance controls for AI workloads, supporting reliable and responsible production operation in alignment with enterprise standards.

Collaboration & Communication:

Works closely with U.S.-based engineering, application, data, and AI teams across time zones, translating platform and infrastructure requirements into technical solutions and surfacing tradeoffs early to keep delivery on track.
Communicates infrastructure decisions, platform tradeoffs, and operational status clearly across technical and non-technical audiences.
Provides technical guidance and peer review to engineers within the Pune team, contributing to overall platform engineering quality through hands-on feedback.

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