Applied AI Engineering Delivery Manager

KANINI Software Solutions•Nashville, TN

2d•Remote

About The Position

We are looking for an experienced Applied AI Engineering Delivery Manager with 10 to 12 years of overall experience to lead the delivery of complex, large-scale technology programs that increasingly leverage AI and agentic tools. This role empowers organizations to build deeper relationships with customers through innovative strategies, advanced analytics, Generative AI, transformative technologies, and creative design, enhancing customer experiences and driving sustained growth and value creation. The Applied AI Engineering Delivery Manager balances business strategy, technology, creativity, and ongoing managed services to solve delivery challenges, while using AI and agentic tools to compress planning, risk-identification, and reporting cycles so the team can focus on the judgment calls AI cannot replace: stakeholder alignment, trade-off decisions, and delivery leadership. This role requires a blend of strong program and people management skills, deep technical delivery experience, and fluent, responsible use of AI to accelerate delivery outcomes.

Requirements

10 to 12 years of consulting and/or industry experience in technology delivery or engineering management roles.
Experience managing large-scale application programs supporting complex business processes and system integrations.
Experience supporting clients in one or more of the following areas: system delivery, application modernization, data integration, or technology operations.
Bachelor’s degree in Computer Science, Engineering, Business, or a related field.
Experience managing mid-to-large size engagements or workstreams and leading multiple team members.
Experience mentoring or leading multiple team members across cross-functional delivery teams.
Hands-on exposure to AI-enabled software delivery, including use of GenAI or agentic tools for requirements analysis, code generation, testing, or documentation.
Understanding of responsible AI practices, including guardrails for hallucination, bias, and drift, and the use of evaluation harnesses before and after deployment.
Ability to travel 0-20%, on average, based on the work you do and the clients and industries/sectors you serve.
Limited immigration sponsorship may be available.
Ability to work independently and collaborate as part of a team.
Effective written and verbal communication skills.
Meticulous attention to detail and quality of work product.
Ability to build and sustain professional relationships.
Ability to lead projects or workstreams.
Ability to manage and prioritize multiple tasks in a fast-paced and dynamic environment.
Strong interpersonal skills and professional demeanor.
Ability to meet deadlines.
Ability to mentor and provide clear guidance to others.

Nice To Haves

Experience with modern agentic AI tools such as Claude Code, Claude Co-work, OpenAI Codex, Cursor, or Visual Studio Code is a plus.

Responsibilities

Manage the end-to-end lifecycle of large-scale application programs, overseeing application performance, reliability, and scalability.
Coordinate system upgrades, patches, and enhancements across the technology landscape.
Ensure seamless integration with related upstream and downstream systems.
Lead and manage projects related to system transformation and modernization, including the adoption of AI-enabled engineering practices.
Develop project plans, timelines, and budgets, using AI tools to accelerate estimation, scenario planning, and risk modeling.
Coordinate with vendors, system integrators, and internal IT teams to ensure aligned delivery.
Monitor project risks, issues, and dependencies, leveraging AI-driven insights to flag emerging risks earlier.
Collaborate with business stakeholders and operations teams to gather requirements and translate them into system functionality.
Act as the liaison between technical teams and business users, ensuring clear and continuous communication.
Manage and mentor application developers, analysts, and support staff, fostering adoption of AI and agentic tools in daily engineering workflows.
Promote Agile/DevOps practices for continuous delivery and improvement, augmented with AI-assisted code review, testing, and release management.
Ensure effective workload planning and team performance across the delivery organization.
Ensure accurate business logic implementation and data integrity across systems.
Oversee reporting and analytics, using AI to automate dashboarding and surface actionable delivery insights.
Support data exchanges and integrations with external and partner systems.
Ensure timely resolution of production issues and incidents, applying AI-assisted root-cause analysis to speed up triage.
Establish SLAs and monitor service performance, using AI-based monitoring and anomaly detection tools.
Drive continuous improvement and process optimization, embedding AI and agentic tools as force-multipliers across the delivery lifecycle.
Evaluate and pilot emerging AI and agentic engineering tools (such as Claude Code, Claude Co-work, GitHub Copilot, or similar) to improve delivery velocity, quality, and cost efficiency.
Establish guardrails and governance for responsible AI use in engineering delivery, including human-in-the-loop review, evaluation, and escalation paths.