AI Lead Engineer

Job Responsibilities

·        Set the strategic direction of Client AI practice, including which capabilities to build in-house, which to source through partners, and how the offering portfolio evolves as the agentic tooling landscape matures.

·        Advise client executives on AI strategy and roadmap, sequencing investments and distinguishing where AI creates defensible business value from where it does not.

·        Work closely with Product Owners, Delivery Managers, Client ai Leadership, and other Engineering Leads to unblock technical challenges by providing direction and solutions.

·        Own technical architecture and AI solution governance across all client engagements and set the standards that make that role repeatable rather than personal.

·        Design and oversee production agentic systems, including intake, outreach, escalation, and feedback-loop agents, with error handling and pre-deployment compliance gates.

·        Build and unblock critical-path work personally across cloud infrastructure, CI/CD, secrets management, integrations, and model development.

·        Lead technical presales by running discovery with prospects, gathering requirements, and building the prototypes that convert opportunities into engagements.

·        Define and lead workstreams where ownership is currently implicit, making scope explicit before committing the business to it.

·        Maintain portfolio discipline across concurrent engagements, closing, merging, or reprioritizing stale work rather than allowing it to accumulate, and operating from program level down to individual tickets as the work requires.

·        Run client-facing AI governance, including participating in steering committees, AI Center of Excellence artifacts, standards and guidelines, and KPI and value frameworks, and report portfolio health and architecture risk to Client leadership.

Required Experience:

·        10+ years of overall software engineering experience, with the last several years focused on designing and developing Generative AI applications.

·        Strong hands-on expertise in building enterprise-grade AI agents, conversational AI solutions, and related architectures.

·        Proven ability to technically lead, mentor, and review the work of other AI engineers.

·        Ability to balance hands-on development with technical oversight, solution design, and delivery accountability.

·        The emphasis should be on finding an exceptional technical leader who is still actively building solutions, rather than someone primarily focused on management, strategy, or coordination.

·        A track record of setting multi-year technical strategy and defending it to executive audiences, including build-versus-buy, platform selection, and partner decisions.

·        Ownership of AI/ML solution architecture on client-facing engagements, carried from presales through to production.

·        Experience advising executive stakeholders on AI investment priorities and roadmap sequencing.

·        Production experience designing multi-agent or agentic AI systems, including orchestration, escalation paths, error handling, and human-in-the-loop feedback.

·        Current hands-on capability with cloud-native delivery: Kubernetes and Helm, containerized CI/CD, AWS, and secrets management patterns.

·        Practical machine learning experience sufficient to review, correct, and ship model work.

·        A track record of establishing AI governance artifacts such as standards, guidelines, ethics charters, and KPI and value frameworks for enterprise or institutional clients.

·        Comfort operating through influence rather than formal authority, including with offshore development centers and third-party vendors.

·        A degree in Computer Science, Engineering, or a related technical field is required.

Technical Skills Required:

·        Kubernetes and Helm; containerized   deployment and promotion

·        AWS, including ECR, Secrets Manager, and External Secrets Operator patterns

·        CI/CD pipeline design, including image build, scanning, and deployment

·        Agentic AI frameworks and LLM application architecture

·        Python and the machine learning toolchain, including XGBoost and model evaluation

·        API design and integration; conversational AI and chatbot platform configuration

·        Encryption standards and secrets management practices

·        Jira for program and sprint management, and portfolio planning tools

Soft Skills Required:

·        Strategic judgment, with the ability to distinguish durable architectural bets from short-term expedients

·        Executive presence with client sponsors and steering committees

·        Consultative approach during internal and external discussions to drive favorable outcomes

·        Clear written and verbal communication, including translating technical risk for non-technical sponsors

·        Credibility with customers and prospects grounded in understanding their business

·        Composure with ambiguity and rapid context-switching across concurrent engagements

·        Ability to work effectively with offshore teams, with the necessary patience and understanding of cultural differences

·        Technical mentorship that raises the capability of surrounding engineers