Transformation Lead - AD

Responsibilities

Client Solution Engineering & Integration (External Focus)

· Lead Technical Discovery & Solution Design: After purchase, partner with the Solutioning team to facilitate in-depth technical workshops with clients. Unpack current workflows, data challenges, and business objectives to define the path forward.

· Architect Custom Integration Plans: Design and document how our platform and services integrate into the client’s existing AI/ML pipelines and infrastructure.

· Serve as the 'Art of the Possible' Advisor: Act as the primary technical point of contact, showing clients how our advanced capabilities, custom tooling, and innovative workflows can address their most challenging data problems and accelerate their AI initiatives.

· Translate Client Needs for the Platform Team: Convert complex client requirements into clear technical specifications and user stories for our core Platform Deployment & Management team, ensuring our platform roadmap reflects real customer needs.

· Oversee Proofs of Concept (POCs): Lead and build targeted POCs that validate solution value and feasibility in the client’s environment, reducing risk for large-scale engagements.

Internal Process Transformation & Innovation (Internal Focus)

· Own Process Excellence: Take responsibility for operational efficiency across our data annotation workforce. Continuously review our Human-in-the-Loop (HITL) workflows to surface and remove bottlenecks.

· Develop AI-Powered 'Co-Pilots' and Accelerators: Architect, build, and deploy a set of internal tools and intelligent agents to support human annotators. Capabilities include automated quality checks, predictive assistance, and workflow-agnostic automation scripts.

· Engineer 'Safety Nets': Build and implement automated quality assurance (QA) systems and predictive monitoring to reliably surpass client SLAs for both accuracy and throughput.

· Drive Human-in-the-Loop Optimization: Use Generative AI, LLMs (fine-tuning, RAG), and computer vision models to develop systems that improve the intelligence, speed, and accuracy of the human workforce.

· Quantify Business Impact: Create and maintain a measurement framework for the ROI of transformation initiatives, clearly articulating effects on project margins, delivery velocity, and client TCO.

Skills & Qualifications

Required Qualifications:

· Experience: 14+ years in a technology field, with at least 5 years in a senior, client-facing technical role such as a Solutions Architect, Forward Deployed Engineer, Professional Services Consultant, or Transformation Lead.

· Applied AI & Process Automation: Demonstrated, hands-on experience building and deploying production-grade AI/ML systems for process automation and optimization. This practical experience is more important than pure research background.

· Generative AI Stack Expertise: Strong, practical experience across the modern Generative AI stack, including LLMs (fine-tuning, RAG), agentic frameworks, and vector databases. You must have built solutions using these technologies.

· Hybrid Skillset: Proven capability to work at the intersection of advanced technical execution and business strategy. You must be able to write code and architect systems, while also building a compelling business case and presenting it to client executives.

· Cloud Architecture: Deep architectural expertise with one or more major cloud platforms (AWS, Azure, GCP), including their managed AI/ML services (e.g., SageMaker, Vertex AI).

Preferred Qualifications:

· Consultative Mindset: A strong ability to listen, troubleshoot, and help clients and internal teams arrive at elegant, high-impact solutions.

· Data Annotation Domain Knowledge: Direct experience with data annotation platforms, data quality issues, and the operational realities of running large-scale data labeling teams.

· Experience Building Internal Tools: Evidence of identifying an internal business requirement and delivering a software solution to address it, resulting in measurable efficiency improvements.

· An advanced degree (master's or PhD) in Computer Science, AI, or a related field is a strong plus.

· A portfolio of projects (e.g., GitHub) or detailed case studies showing your experience building AI-driven solutions.