Senior Cloud Platform and AI Engineer

Infosys•New York, TX

About The Position

The Infosys Engineering unit focuses on product-centric value delivery through innovative engineering solutions, using data-first engineering and value-stream management alongside AI, machine learning, and predictive analytics. The Senior Cloud Platform and AI Engineer will design, develop, automate, and operate secure, scalable cloud and AI platforms across AWS and Azure. The role builds Python and .NET/C# REST APIs, backend services, and reusable application templates for application onboarding, platform integration, and cloud automation. It develops reusable Terraform modules and Helm deployments for Kubernetes platforms and related infrastructure, and integrates generative AI capabilities with enterprise applications and developer workflows. The engineer creates and maintains CI/CD workflows, implements security and reliability controls, supports production systems, and troubleshoots cloud, application, AI, and deployment issues. The role also collaborates across engineering, cloud, security, database, and operations teams; maintains architecture and operational documentation; provides technical leadership; mentors engineers; and advances reusable engineering standards and self-service platform capabilities. The position is assigned the job role of Technology Consultant 2. Responsibilities span the software development lifecycle, from requirements elicitation and design through coding, testing, integration, production support, and documentation. Infosys requires more than 16 years of information technology experience, with strong hands-on experience in cloud architecture, DevOps, site reliability engineering, platform engineering, and production operations. A bachelor's degree or foreign equivalent from an accredited institution is required, though three years of progressive specialty experience may be considered in lieu of each year of education. The position may require relocation and/or travel to the work or project location. Applicants authorized to work in the United States are encouraged to apply.

Requirements

More than 16 years of information technology experience.
Strong hands-on experience in cloud architecture, DevOps, site reliability engineering, platform engineering, and production operations.
Practical expertise in AWS and Azure cloud architecture.
Practical expertise with Kubernetes, including EKS, AKS, ECS, and OpenShift.
Practical expertise with Terraform, Helm, Ansible, and multi-cloud infrastructure automation.
Practical expertise in Python and .NET/C# backend development, REST APIs, and FastAPI.
Practical expertise with GitHub Actions, Jenkins, Azure DevOps, Concourse, Argo CD, and Harness.
Practical expertise with Docker and Linux.
Practical expertise in IAM, RBAC, networking, security, high availability, disaster recovery, monitoring, and production operations.
Experience building cloud automation, reusable platform services, and secure enterprise deployment patterns.
Experience leading technical initiatives and collaborating with globally distributed engineering, security, product, and operations teams.
Bachelor’s degree or foreign equivalent from an accredited institution; three years of progressive experience in the specialty may be considered in lieu of each year of education.
Collaborative spirit, excellent communication skills, and ability to work across cross-functional teams.
Ability to handle end-to-end SDLC phases from requirements gathering through implementation.
Ability to translate complex requirements into actionable development tasks.
Hands-on coding experience and a proactive approach to testing, troubleshooting, and refining applications.

Nice To Haves

Hands-on experience with generative AI services and Model Context Protocol capabilities.
Experience with Amazon Bedrock and SageMaker.
Experience with large language models, retrieval-augmented generation, embeddings, vector databases, AI agents, MCP servers, FastMCP, tools, skills, and plugins.
Experience with Prometheus, Grafana, ELK, Splunk, and Azure Monitor.
Experience with AWS Lambda and Azure Functions.
Experience with PostgreSQL and MongoDB.
Experience with OPA/Gatekeeper and Terraform Sentinel.
Experience with multi-region and active-passive deployments.
Experience with SonarQube, Artifactory, Octopus Deploy, and ServiceNow.

Responsibilities

Document assigned portions of business requirements as part of requirements elicitation.
Facilitate software application design discussions and document decisions to guide technical implementation.
Design, develop, automate, and operate secure, scalable cloud and AI platforms across AWS and Azure.
Build Python and .NET/C# REST APIs, backend services, and reusable application templates for application onboarding, platform integration, and cloud automation.
Develop reusable Terraform modules and Helm deployments for Kubernetes platforms, IAM, serverless services, networking, monitoring, and applications.
Integrate generative AI services and capabilities, including large language models, retrieval-augmented generation, embeddings, vector databases, AI agents, and Model Context Protocol services, with enterprise applications and developer workflows.
Create and maintain CI/CD workflows using the specified pipeline tools.
Implement access controls, policy enforcement, vulnerability remediation, observability, high availability, disaster recovery, multi-region deployment, and production support.
Participate in coding and integrate new features or updates into existing applications while maintaining system stability.
Conduct code reviews, make changes to the codebase, and maintain code repositories.
Implement test strategies, analyze results, and coordinate bug fixes to uphold software quality standards.
Troubleshoot APIs, AI integrations, Kubernetes, cloud infrastructure, authentication, networking, databases, and deployment pipelines.
Resolve production issues and recommend preventive strategies to improve system reliability.
Develop user training programs, documentation, and support frameworks to facilitate transitions to new software applications.
Collaborate with development, cloud, security, database, and operations teams, including globally distributed teams.
Maintain architecture documents, migration plans, runbooks, operational procedures, and records of code, testing, and support activities.
Provide technical leadership, define reusable engineering standards, mentor engineers, and drive self-service platform capabilities.
Handle end-to-end SDLC phases, translate complex requirements into actionable development tasks, and support software integration across cross-functional teams.