Transformation Lead - AD

Responsibilities

Client Solution Engineering & Integration (External Focus)

· Lead Technical Discovery & Solution Design: Post-sale, partner with the Solutioning team to lead deep-dive technical workshops with clients. Deconstruct their current workflows, data challenges, and business objectives.

· Architect Custom Integration Plans: Design and map out how our platform and services will integrate seamlessly into the client's existing AI/ML pipelines and infrastructure.

· Act as the Art of the Possible Guide: Serve as the primary technical advisor to clients, demonstrating how our advanced capabilities, custom tools, and innovative workflows can solve their most difficult data challenges and accelerate their AI initiatives.

· Bridge to the Platform Team: Translate complex client requirements into precise technical specifications and user stories for our core Platform Deployment & Management team, ensuring our platform roadmap is directly informed by real-world client needs.

· Oversee Proofs of Concept (POCs): Lead and develop targeted POCs to prove the value and viability of our solutions within the client's environment, de-risking large-scale engagements.

Internal Process Transformation & Innovation (Internal Focus)

· Drive Process Excellence: Be the owner of operational efficiency for our data annotation workforce. Continuously analyze our Human-in-the-Loop (HITL) workflows to identify and eliminate bottlenecks.

· Develop AI-Powered Co-Pilots and Accelerators: Architect, build, and deploy a portfolio of internal tools and intelligent agents that augment our human annotators. This includes automated quality checks, predictive assistance, and workflow-agnostic automation scripts.

· Engineer Safety Nets: Design and implement automated quality assurance (QA) systems and predictive monitoring to ensure we consistently exceed client SLAs for accuracy and throughput.

· Champion Human-in-the-Loop Optimization: Leverage Generative AI, LLMs (fine-tuning, RAG), and computer vision models to create systems that make our human workforce smarter, faster, and more accurate.

· Quantify Business Impact: Develop and maintain a framework to measure the ROI of your transformation initiatives, clearly demonstrating their impact on project margins, delivery speed, 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: Proven, hands-on experience building and deploying production-grade AI/ML systems specifically for process automation and optimization. This is more important than pure research experience.

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

· Hybrid Skillset: A demonstrated ability to operate at the intersection of deep technology and business strategy. You must be able to write code and architect systems, but also create a compelling business case and present to a client executive.

· Cloud Architecture: Deep architectural expertise in 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 natural ability to listen, diagnose problems, and guide clients and internal teams toward elegant, effective solutions.

· Data Annotation Domain Knowledge: Direct experience with data annotation platforms, data quality challenges, and the operational dynamics of large-scale data labeling workforces.

· Experience Building Internal Tools: A track record of identifying an internal business need and building a software solution to address it, driving measurable efficiency gains.

· 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 demonstrating your work in building AI-driven solutions.