AI Principal Technical Consultant, AI Services

AHEAD builds platforms for digital business. By weaving together advances in cloud infrastructure, automation and analytics, and software delivery, we help enterprises deliver on the promise of digital transformation. At AHEAD, we prioritize creating a culture of belonging, where all perspectives and voices are represented, valued, respected, and heard. We create spaces to empower everyone to speak up, make change, and drive the culture at AHEAD. We are an equal opportunity employer, and do not discriminate based on an individual's race, national origin, color, gender, gender identity, gender expression, sexual orientation, religion, age, disability, marital status, or any other protected characteristic under applicable law, whether actual or perceived. We embrace all candidates that will contribute to the diversification and enrichment of ideas and perspectives at AHEAD. AHEAD is seeking a Principal Technical Consultant, AI Services to lead the architecture, engineering, and deployment of enterprise-grade AI solutions for our clients. This is a senior hands-on technical leadership role for someone who can turn ambiguous business problems into scalable, secure, production-ready AI systems. You will work directly with client technology and business leaders to define solution architecture, guide engineering teams, make technical trade-offs, and ensure that AI initiatives move from prototype to measurable business impact. The ideal candidate combines software engineering depth, applied AI / ML fluency, enterprise architecture judgment, and consulting-style client leadership. You do not need to be a pure research scientist, but you should be credible with engineers, data scientists, architects, platform teams, security stakeholders, and senior executives. This role is well suited for candidates with backgrounds in applied AI consulting, ML engineering, AI solution architecture, technical product development, data science engineering, or advanced analytics engineering environments. The compensation range indicated in this posting reflects the On-Target Earnings (“OTE”) for this role, which includes a base salary and any applicable target bonus amount. This OTE range may vary based on the candidate’s relevant experience, qualifications, and geographic location. Why AHEAD: Through our daily work and internal groups like Moving Women AHEAD and RISE AHEAD, we value and benefit from diversity of people, ideas, experience, and everything in between. We fuel growth by stacking our office with top-notch technologies in a multi-million-dollar lab, by encouraging cross department training and development, sponsoring certifications and credentials for continued learning. USA Employment Benefits include: - Medical, Dental, and Vision Insurance - 401(k) - Paid company holidays - Paid time off - Paid parental and caregiver leave - Plus more! See benefits https://www.aheadbenefits.com/ for additional details. Use of AI: We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, assessing responses, or to capture recordings and create transcriptions or summaries during interviews. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please refer to the Candidate Privacy Notice or contact us at [email protected]. You may opt-out of the review or analysis of your application and resume by AI tools by using the General Application. Please include the role you wish to apply for in the Additional Information field. You may also choose to opt-out of recording and transcription at any time, including after joining an interview.  Candidates will not be penalized for choosing to opt-out. 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.

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      "text": "What You’ll Do",
      "content": "<div>\n<h3><strong>1. Architect and build enterprise AI solutions</strong></h3>\n\n<li>Lead the architecture, design, development, and deployment of enterprise-grade AI, GenAI, agentic, automation, and ML-enabled solutions.</li>\n<li>Translate ambiguous business and technical requirements into clear solution designs, architecture decisions, implementation plans, and engineering workstreams.</li>\n<li>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.</li>\n<li>Make practical architecture decisions across models, data pipelines, APIs, orchestration layers, vector stores, enterprise applications, security controls, and deployment environments.</li>\n<li>Ensure solutions are scalable, secure, maintainable, observable, and aligned to measurable client outcomes.</li>\n\n<h3><strong>2. Lead technical delivery across client engagements</strong></h3>\n\n<li>Lead technical workstreams across one or more client engagements, including estimation, planning, design, build, testing, deployment, risk management, and issue resolution.</li>\n<li>Serve as the technical authority for project teams, owning solution quality, engineering standards, and technical decision-making.</li>\n<li>Partner with client engineering, data, cloud, security, and platform teams to integrate AI solutions into enterprise environments.</li>\n<li>Lead technical workshops, architecture sessions, demos, design reviews, and working sessions with both technical and non-technical stakeholders.</li>\n<li>Communicate complex technical concepts clearly to senior business and technology leaders.</li>\n\n<h3><strong>3. Establish production-grade AI engineering standards</strong></h3>\n\n<li>Define and apply strong engineering practices across code quality, automated testing, CI/CD, observability, monitoring, reliability, scalability, security, and maintainability.</li>\n<li>Establish practical patterns for LLMOps / MLOps, model integration, prompt and workflow management, evaluation, guardrails, performance monitoring, and responsible AI usage.</li>\n<li>Design AI systems with appropriate controls for privacy, security, governance, compliance, auditability, and human oversight.</li>\n<li>Build and improve reusable components, reference architectures, deployment patterns, and accelerators that strengthen AHEAD’s AI delivery capability.</li>\n<li>Ensure pilots are built with a credible path to production and scale, not as isolated demos.</li>\n\n<h3><strong>4. Partner across strategy, business, and technical teams</strong></h3>\n\n<li>Work with strategy consultants, solution managers, architects, engineers, and client stakeholders to connect business priorities with technical execution.</li>\n<li>Help clients assess trade-offs across speed, cost, risk, usability, accuracy, reliability, and long-term maintainability.</li>\n<li>Shape technical roadmaps that sequence pilots, platform enablers, integration work, governance requirements, and scale-up activities.</li>\n<li>Help define success metrics for AI solutions, including business impact, adoption, model/application quality, reliability, and operational performance.</li>\n<li>Act as a bridge between executive ambition and engineering reality.</li>\n\n<h3><strong>5. Mentor teams and build the AI Services practice</strong></h3>\n\n<li>Coach engineers, consultants, and technical specialists on solution design, engineering quality, client communication, and delivery excellence.</li>\n<li>Review technical designs and code to ensure high-quality, maintainable, production-ready output.</li>\n<li>Contribute to AHEAD’s AI offerings, technical methods, architecture standards, accelerators, and thought leadership.</li>\n<li>Support pre-sales and solution shaping by helping define technical scope, delivery approach, effort estimates, risks, and implementation plans.</li>\n<li>Help elevate AHEAD’s reputation as a firm that can not only advise on AI, but build and scale it in enterprise environments.</li>\n\n</div>"
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      "text": "What You’ll Bring",
      "content": "<div>\n<h3><strong>Required qualifications</strong></h3>\n\n<li>Typically <strong>7–12+ years of experience</strong> in software engineering, ML engineering, applied AI, data science engineering, AI solution architecture, technical consulting, or enterprise technology delivery.</li>\n<li>Strong hands-on engineering experience, especially with <strong>Python</strong>, APIs, cloud-native development, data integration, workflow automation, and enterprise system integration.</li>\n<li>Experience designing, building, and deploying production-grade AI, GenAI, ML, automation, or advanced analytics solutions.</li>\n<li>Practical familiarity with AI solution patterns such as RAG, LLM application design, agentic workflows, orchestration, model integration, vector databases, evaluation, guardrails, and observability.</li>\n<li>Strong understanding of modern engineering practices, including CI/CD, automated testing, version control, containerization, monitoring, reliability, security, and scalable deployment.</li>\n<li>Ability to lead technical teams, review designs and code, mentor engineers, and drive delivery quality across complex workstreams.</li>\n<li>Strong client-facing communication skills, including the ability to explain technical trade-offs clearly to engineering teams, executives, and non-technical stakeholders.</li>\n<li>Ability to operate in ambiguous environments, structure technical problems, make sound architecture decisions, and guide teams toward practical outcomes.</li>\n\n<h3><strong>Preferred qualifications</strong></h3>\n\n<li>Experience in applied AI consulting, advanced analytics consulting, ML engineering, AI product development, or enterprise AI platform delivery.</li>\n<li>Background from a high-performing consulting, technology, AI, cloud, data, or software engineering organization.</li>\n<li>Experience with cloud and data platforms such as Azure, AWS, GCP, Databricks, Snowflake, Kubernetes, or similar enterprise platforms.</li>\n<li>Experience with LLM frameworks, orchestration tools, vector databases, model serving, ML platforms, or AI governance tooling.</li>\n<li>Experience moving AI solutions from prototype or pilot into production environments.</li>\n<li>Experience supporting technical pre-sales, solution shaping, architecture proposals, or executive-level technical advisory.</li>\n<li>Advanced degree in computer science, engineering, data science, applied mathematics, or a related field is a plus.</li>\n\n<h3><strong>What is not required</strong></h3>\n\n<li>You do <strong>not</strong> need to be a pure AI researcher.</li>\n<li>You do <strong>not</strong> need to have deep academic ML specialization.</li>\n<li>You do <strong>not</strong> need to be only a platform architect or only a data scientist.</li>\n<li>You do need to be a strong technical builder and leader who can design, guide, and deliver enterprise-grade AI solutions.</li>\n\n</div>"
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