AI Application Engineering & DevSecOps - Delivery Lead

Job purpose ? The Lead AI Application Engineering DevSecOps Consultant is responsible for creating a controlled and? repeatable pathway that transforms business-built AI prototypes into secure, production-grade risk? applications.? Working closely with risk practitioners, business builders, platform architects, and cyber teams, the role? establishes the engineering standards, testing practices, security controls, deployment patterns, and? governance guardrails required for enterprise adoption. The consultant acts as the production-readiness? authority for AI agents, reviewing, testing, hardening, and preparing applications for Azure deployment while? enabling business users to continue innovating responsibly.? The role ensures all Risk AI agents operate under common engineering standards, with practical controls? covering GitHub, testing, secrets management, access control, documentation, monitoring, and release? management, creating solutions that are reliable, auditable, secure, and maintainable.? Key responsibilities ? ? Assess AI agents and application codebases for production readiness across architecture, code quality, security,? testing, and deployment.? ? Identify and remediate engineering, security, and operational gaps in business-built AI prototypes.? ? Harden applications through secure authentication, authorization, secrets management, input validation, and? safe AI model integration practices.? ? Refactor prototype solutions into maintainable, scalable, and enterprise-ready applications with robust? engineering standards.? ? Establish coding standards, repository governance, branching strategies, pull-request processes, and code? review practices.? ? Collaborate with cloud, infrastructure, security, and architecture teams to ensure compliant and scalable? deployments.? ? Develop reusable application templates, starter kits, reference architectures, and implementation checklists.? ? Ensure consistent integration of AI applications with enterprise platforms, data services, and architectural? standards.? ? Design, implement, and optimize CI/CD pipelines using GitHub Actions, Azure DevOps, and approved enterprise? tooling.? ? Define and implement secure Azure deployment patterns covering App Services, Functions, Container Apps,? API Management, Key Vault, and Managed Identities.? ? Establish comprehensive testing frameworks including unit, integration, regression, end-to-end, user? acceptance, and AI-specific testing.? ? Implement AI assurance practices including prompt testing, response evaluation, adversarial testing, and model? output validation.? ? Define quality benchmarks, golden datasets, confidence thresholds, and auditability standards for AI? applications.? ? Embed Secure SDLC practices, threat modeling, vulnerability management, dependency scanning, and least? privilege access controls.? ? Define standards for application monitoring, logging, alerting, resilience, and operational support.? ? Deliver governance, training, workshops, and developer enablement initiatives that accelerate innovation? while maintaining control.? Key competencies? ? Conduct architecture reviews and code reviews to improve quality, security, and maintainability.? ? Coach and mentor business developers, citizen developers, and risk practitioners on secure software? engineering practices.? Technical Competencies? ? Candidates should have a B.E./B.Tech/MCA/MBA in Information Systems, Computer Science or a related field? ? Strong hands-on expertise in React, TypeScript, JavaScript, Node.js, Python, and modern web application? frameworks.? ? Proven experience building, deploying, and supporting enterprise web applications, APIs, and microservices.? ? Deep knowledge of Azure cloud services including App Service, Functions, Container Apps, API Management,? Key Vault, Managed Identity, and Azure Monitor.? ? Strong DevOps and CI/CD experience using GitHub Enterprise, GitHub Actions, Azure DevOps, and automated? deployment pipelines.? ? Knowledge of AI application risks including prompt injection, hallucination management, model evaluation, and? AI testing methodologies.? ? Expertise in application architecture, software engineering best practices, and production support models.? ? Strong understanding of API design, service-to-service authentication, authorization, and API governance.? ? Hands-on experience with secure application development, threat modeling, secure SDLC, vulnerability? management, and security engineering practices.? ? Experience implementing logging, monitoring, observability, incident management, and operational controls.? Preferred Competencies? ? Experience building and operationalizing AI, GenAI, LLM, RAG, and agent-based applications.? ? Experience with frameworks such as FastAPI, Flask, Streamlit, LangChain, Semantic Kernel, and Azure AI? services.? ? Experience in financial services, risk management, compliance, or other highly regulated environments.? ? Familiarity with enterprise data governance, semantic layers, and controlled data-access patterns.? ? Leadership Behavioral Competencies? ? Strong stakeholder management and cross-functional collaboration skills.? ? Ability to influence engineering practices without direct authority.? ? Strong coaching, mentoring, and developer enablement capabilities.? ? Pragmatic approach to governance, balancing innovation, speed, and control.? ? Excellent problem-solving and analytical skills.? ? Strong written and verbal communication skills with both business and technical audiences.? ? Ability to drive engineering discipline, quality, and continuous improvement across distributed teams.? Disclaimer : This job posting has been aggregated from external source. Role details, content, and availability are subject to change. Applicants are advised to confirm the latest information directly on the company website before applying.