AI Principal Technical Consultant, AI Services

What You’ll Do

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.
2. 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.
3. 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.
4. 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.
5. 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.

What You’ll Bring

Required qualifications
Typically 7–12+ years of experience in software engineering, ML engineering, applied AI, data science engineering, AI solution architecture, technical consulting, or enterprise technology delivery.
Strong hands-on engineering experience, especially with Python, APIs, cloud-native development, data integration, workflow automation, and enterprise system integration.
Experience designing, building, and deploying production-grade AI, GenAI, ML, automation, or advanced analytics solutions.
Practical familiarity with AI solution patterns such as RAG, LLM application design, agentic workflows, orchestration, model integration, vector databases, evaluation, guardrails, and observability.
Strong understanding of modern engineering practices, including CI/CD, automated testing, version control, containerization, monitoring, reliability, security, and scalable deployment.
Ability to lead technical teams, review designs and code, mentor engineers, and drive delivery quality across complex workstreams.
Strong client-facing communication skills, including the ability to explain technical trade-offs clearly to engineering teams, executives, and non-technical stakeholders.
Ability to operate in ambiguous environments, structure technical problems, make sound architecture decisions, and guide teams toward practical outcomes.
Preferred qualifications
Experience in applied AI consulting, advanced analytics consulting, ML engineering, AI product development, or enterprise AI platform delivery.
Background from a high-performing consulting, technology, AI, cloud, data, or software engineering organization.
Experience with cloud and data platforms such as Azure, AWS, GCP, Databricks, Snowflake, Kubernetes, or similar enterprise platforms.
Experience with LLM frameworks, orchestration tools, vector databases, model serving, ML platforms, or AI governance tooling.
Experience moving AI solutions from prototype or pilot into production environments.
Experience supporting technical pre-sales, solution shaping, architecture proposals, or executive-level technical advisory.
Advanced degree in computer science, engineering, data science, applied mathematics, or a related field is a plus.
What is not required
You do not need to be a pure AI researcher.
You do not need to have deep academic ML specialization.
You do not need to be only a platform architect or only a data scientist.
You do need to be a strong technical builder and leader who can design, guide, and deliver enterprise-grade AI solutions.