Insight Global
Website:
insightglobal.com
Company:
https://www.linkedin.com/company/insight-global
Seniority: Entry level
Industries: Business Consulting and Services
Job details:
Minimum Qualifications
- Bachelor’s degree in information technology, Computer Science, Computer Information Systems, Software Engineering, Mathematics, Statistics or related field of study or equivalent, relevant work experience
- 2-5 years of related work experience in hands-on programming experience with one or more modern programming languages. Experience designing and building scalable automation solutions, engineering platforms, developer tools, reusable components, shared services, or self-service capabilities and relevant experience working across areas of the Platform engineering.
Preferred Qualifications
- Relevant industry certifications in cloud platforms, DevOps, platform engineering, software development, automation, AI/ML, data engineering, or related technologies are preferred.
Skills
- Platform Engineering & Architecture
- Developer Experience (DevEx) & Self-Service Platforms
- Reusable Components, APIs, SDKs & Shared Services
- Automation & AIDLC Enablement
- AI-Assisted Engineering & AI Agents
- Cloud Engineering (AWS) & Cloud-Native Platforms
- DevOps, CI/CD
- Monitoring, Observability & Reliability Engineering
- RAG Systems, MCP Integrations & AI Tool Integration (GitHub Copilot, AWS Kiro, Bedrock)
- Observability, Debugging & Failure Analysis
- LLM Workflows, Prompt Engineering & Context Engineering
- AI Agents, Agent Orchestration & Workflow Automation
The Senior Associate Platform Engineer contributes to the development, configuration, and support of platform services, automation solutions, and cloud-based infrastructure. Assists in building and maintaining automation frameworks, reusable tools, and platform capabilities that improve engineering efficiency, reliability, and software delivery. Supports the implementation of CI/CD workflows, infrastructure automation, and platform observability while adopting modern engineering practices and AI-enabled tools under guidance. Collaborates with engineering, product, and platform teams to deliver scalable solutions, streamline development workflows, and support continuous improvement across the AI-driven development lifecycle.
This role specializes in SDLC automation and intelligent engineering, focusing on building reusable frameworks, components, and self-service capabilities that accelerate software delivery. Engineers leverage modern programming languages, automation platforms, data-driven insights, CI/CD integration, and AI-powered tools to improve platform reliability, reduce maintenance effort, enhance developer productivity, and enable autonomous workflows across the AIDLC.
Essential Job Functions
- Platform Engineering & Platform Capabilities: Design and maintain scalable engineering platforms, products, and shared services. Build reusable APIs, SDKs, frameworks, and self-service capabilities. Improve developer productivity, operational efficiency, and scalability. Establish secure delivery guardrails and standards. Reduce manual effort through automation and reusable solutions.
- Intelligent Automation, AI Enabled Engineering: Design and implement intelligent automation and AI-driven engineering solutions. Build scalable automation capabilities, platform services, AI agents, and engineering tools. Drive adoption of automation-first and AI-assisted engineering practices. Automate development, testing, deployment, operations, governance, and security processes. Establish reusable automation patterns and workflows. Partner with teams to embed AI, automation, and guardrails across software delivery.
- AI Engineering & Agent Platforms: Build AI-powered engineering capabilities, workflows, and developer experiences. Develop AI agents and autonomous workflows that improve productivity. Integrate LLMs, prompt engineering, context management, and AI orchestration frameworks. Implement MCP, RAG, tool integrations, and agent-based solutions. Evaluate and adopt emerging AI technologies and frameworks.
- Developer Experience & Self-Service Engineering: Design and enhance internal developer platforms that simplify engineering workflows. Build self-service capabilities that reduce dependencies and accelerate delivery. Improve onboarding, development, testing, deployment, and support experiences. Develop reusable templates, automation assets, and engineering accelerators. Promote platform adoption through documentation and enablement. Advocate developer-centric design and continuous improvement.
- Platform Operations, Reliability & Delivery Enablement: Integrate platform capabilities into engineering workflows, CI/CD pipelines, and operations. Build solutions that improve reliability, resilience, scalability, and operational readiness. Implement automated controls, governance, deployment guardrails, and policy enforcement. Enable intelligent operations through automation, AI-assisted insights, and root-cause analysis. Leverage IaC, cloud-native architectures, and automation. Support adoption of modern platform engineering and DevOps practices.
- Security, Governance & Engineering Controls: Build security controls and governance capabilities into engineering platforms. Implement secure-by-design principles and automation across the software delivery lifecycle. Develop automated compliance validation, policy enforcement, and risk management capabilities. Establish guardrails that balance engineering agility with organizational requirements. Partner with security and architecture teams to align with enterprise standards. Continuously improve platform security through automation and proactive practices.
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