Job Summary
AI & Automation Lead – Application Modernization
Role Summary
Lead the design and implementation of AI-driven and automation solutions across application assessment, modernization, migration, testing, deployment, and operations. The role focuses on using Generative AI, intelligent automation, DevOps, and cloud-native capabilities to accelerate modernization and reduce manual effort.
Key Responsibilities
- Define the AI and automation strategy for application modernization and AWS migration.
- Identify automation opportunities across application discovery, assessment, code modernization, testing, deployment, monitoring, and operational support.
- Design and implement Generative AI/LLM-based solutions, including RAG, AI agents, and intelligent workflows where appropriate.
- Evaluate AI-assisted approaches for legacy code analysis, documentation, code transformation, test generation, and migration assessment.
- Lead development of automation frameworks using Python, APIs, scripting, and workflow/orchestration technologies.
- Drive automation of CI/CD, Infrastructure as Code, application deployment, testing, and environment provisioning.
- Integrate AI and automation solutions with AWS services, enterprise applications, APIs, and data sources.
- Establish appropriate controls for AI security, data privacy, access, model evaluation, and responsible AI usage.
- Define reusable automation patterns, accelerators, and frameworks that can be applied across multiple migration waves.
- Measure automation effectiveness through indicators such as manual-effort reduction, execution reliability, defect reduction, and deployment efficiency.
- Provide technical leadership to AI developers, automation engineers, DevOps engineers, and application SMEs.
Required Skills & Qualifications
- Strong experience in AI/ML, Generative AI, automation, or intelligent application development.
- Strong programming skills in Python and experience working with REST APIs.
- Understanding of LLMs, prompt engineering, RAG, embeddings, vector search, and AI agents.
- Experience with AI platforms such as Amazon Bedrock, Amazon SageMaker, Azure AI, or equivalent enterprise AI platforms.
- Strong knowledge of AWS cloud architecture and services.
- Experience with CI/CD, Git, Jenkins/GitHub Actions, Docker, Kubernetes, Terraform/CloudFormation, or equivalent technologies.
- Knowledge of test automation and tools such as Playwright or Selenium is advantageous.
- Understanding of enterprise application modernization, APIs, microservices, and integration patterns.
- Strong technical leadership, architecture, problem-solving, and stakeholder-management skills.
I positioned this as a technical leadership role rather than only an automation-testing role, covering GenAI + automation + AWS + DevOps + application modernization, which better complements the Cloud & Application Modernization Lead, Assessment Consultant, Functional Tester, and Test Automation roles.
Key Responsibilities
AI & Automation Lead – Application Modernization
Role Summary
Lead the design and implementation of AI-driven and automation solutions across application assessment, modernization, migration, testing, deployment, and operations. The role focuses on using Generative AI, intelligent automation, DevOps, and cloud-native capabilities to accelerate modernization and reduce manual effort.
Key Responsibilities
- Define the AI and automation strategy for application modernization and AWS migration.
- Identify automation opportunities across application discovery, assessment, code modernization, testing, deployment, monitoring, and operational support.
- Design and implement Generative AI/LLM-based solutions, including RAG, AI agents, and intelligent workflows where appropriate.
- Evaluate AI-assisted approaches for legacy code analysis, documentation, code transformation, test generation, and migration assessment.
- Lead development of automation frameworks using Python, APIs, scripting, and workflow/orchestration technologies.
- Drive automation of CI/CD, Infrastructure as Code, application deployment, testing, and environment provisioning.
- Integrate AI and automation solutions with AWS services, enterprise applications, APIs, and data sources.
- Establish appropriate controls for AI security, data privacy, access, model evaluation, and responsible AI usage.
- Define reusable automation patterns, accelerators, and frameworks that can be applied across multiple migration waves.
- Measure automation effectiveness through indicators such as manual-effort reduction, execution reliability, defect reduction, and deployment efficiency.
- Provide technical leadership to AI developers, automation engineers, DevOps engineers, and application SMEs.
Required Skills & Qualifications
- Strong experience in AI/ML, Generative AI, automation, or intelligent application development.
- Strong programming skills in Python and experience working with REST APIs.
- Understanding of LLMs, prompt engineering, RAG, embeddings, vector search, and AI agents.
- Experience with AI platforms such as Amazon Bedrock, Amazon SageMaker, Azure AI, or equivalent enterprise AI platforms.
- Strong knowledge of AWS cloud architecture and services.
- Experience with CI/CD, Git, Jenkins/GitHub Actions, Docker, Kubernetes, Terraform/CloudFormation, or equivalent technologies.
- Knowledge of test automation and tools such as Playwright or Selenium is advantageous.
- Understanding of enterprise application modernization, APIs, microservices, and integration patterns.
- Strong technical leadership, architecture, problem-solving, and stakeholder-management skills.
I positioned this as a technical leadership role rather than only an automation-testing role, covering GenAI + automation + AWS + DevOps + application modernization, which better complements the Cloud & Application Modernization Lead, Assessment Consultant, Functional Tester, and Test Automation roles.