AI Principal Technical Consultant, AI Services

1. Architect and build enterprise AI solutions

Lead the architecture, design, development, and deployment of enterprise-grade AI, GenAI, agentic, automation, and ML-enabled solutions.

Translate ambiguous business and technical requirements into clear solution designs, architecture decisions, implementation plans, and engineering workstreams.

Design and build AI solution patterns such as retrieval-augmented generation, workflow orchestration, agent-assisted processes, model integration, API-based automation, and human-in-the-loop review.

Make practical architecture decisions across models, data pipelines, APIs, orchestration layers, vector stores, enterprise applications, security controls, and deployment environments.

Ensure solutions are scalable, secure, maintainable, observable, and aligned to measurable client outcomes.

1. Lead technical delivery across client engagements

Lead technical workstreams across one or more client engagements, including estimation, planning, design, build, testing, deployment, risk management, and issue resolution.

Serve as the technical authority for project teams, owning solution quality, engineering standards, and technical decision-making.

Partner with client engineering, data, cloud, security, and platform teams to integrate AI solutions into enterprise environments.

Lead technical workshops, architecture sessions, demos, design reviews, and working sessions with both technical and non-technical stakeholders.

Communicate complex technical concepts clearly to senior business and technology leaders.

1. Establish production-grade AI engineering standards

Define and apply strong engineering practices across code quality, automated testing, CI/CD, observability, monitoring, reliability, scalability, security, and maintainability.

Establish practical patterns for LLMOps / MLOps, model integration, prompt and workflow management, evaluation, guardrails, performance monitoring, and responsible AI usage.

Design AI systems with appropriate controls for privacy, security, governance, compliance, auditability, and human oversight.

Build and improve reusable components, reference architectures, deployment patterns, and accelerators that strengthen AHEAD’s AI delivery capability.

Ensure pilots are built with a credible path to production and scale, not as isolated demos.

1. Partner across strategy, business, and technical teams

Work with strategy consultants, solution managers, architects, engineers, and client stakeholders to connect business priorities with technical execution.

Help clients assess trade-offs across speed, cost, risk, usability, accuracy, reliability, and long-term maintainability.

Shape technical roadmaps that sequence pilots, platform enablers, integration work, governance requirements, and scale-up activities.

Help define success metrics for AI solutions, including business impact, adoption, model/application quality, reliability, and operational performance.

Act as a bridge between executive ambition and engineering reality.

1. Mentor teams and build the AI Services practice

Coach engineers, consultants, and technical specialists on solution design, engineering quality, client communication, and delivery excellence.

Review technical designs and code to ensure high-quality, maintainable, production-ready output.

Contribute to AHEAD’s AI offerings, technical methods, architecture standards, accelerators, and thought leadership.

Support pre-sales and solution shaping by helping define technical scope, delivery approach, effort estimates, risks, and implementation plans.

Help elevate AHEAD’s reputation as a firm that can not only advise on AI, but build and scale it in enterprise environments.