About the job
About the Role
As the Director of Applied AI Solutions reporting directly to the CTO, you will serve as the senior, client-focused technologist responsible for transforming business opportunities into fully functional AI/ML and Generative AI solutions on leading cloud platforms such as AWS, Azure, and Google Cloud. Your role involves collaborating with C-suite executives, designing comprehensive solutions that encompass everything from data ingestion to implementing responsible AI practices, while guiding interdisciplinary engineering teams through the entire lifecycle from development to deployment and ongoing optimization. You will be instrumental in delivering exceptional outcomes for our clients by leveraging our partnerships with top AI, Data, and Security independent software vendors.
Key Responsibilities
Facilitate discovery workshops to identify AI, ML, and GenAI use cases.
Create business cases, total cost of ownership (TCO) and return on investment (ROI) models, and executive roadmaps.
Design cloud-native architectures that encompass data pipelines, feature stores, model training, vector databases, and real-time inference.
Cultivate expert relationships with AWS, Azure, GCP, and prominent AI ISV partners.
Represent the organization at industry conferences, webinars, and panel discussions.
Publish white papers, present at conferences, and engage with analysts to position the firm as a leader in applied AI innovation.
Monitor emerging research in areas such as LLM fine-tuning, agent frameworks, and privacy-preserving ML, and integrate findings into our offerings.
Qualifications
A minimum of 8 years of experience in data science, ML engineering, or AI architecture.
Proven track record in delivering applied AI (traditional ML, deep learning, Generative AI/LLMs) on at least two major cloud platforms.
At least 2 years of progressive experience in cloud architecture, consulting, or solutions engineering, with 5+ years leading cross-functional technical teams.
Hands-on experience with MLOps stacks and Infrastructure as Code (IaC).
Experience integrating data platforms such as Databricks or Snowflake is a plus.
Strong understanding of DevSecOps, IaC, containerization, CI/CD, Site Reliability Engineering (SRE), and FinOps.
Demonstrated ability to effectively communicate with C-suite stakeholders.
Relevant certifications (e.g., AWS ML Specialty, Azure AI Engineer, Google Professional ML Engineer) are highly desirable.
Bachelor's degree in Computer Science, Data Science, or a related field is required; a Master's or PhD is preferred.
Why You'll Love It Here
Our People: Join a team of innovative thinkers and problem solvers.
Impact: Be at the forefront of AI technology and make a difference.
Growth: Opportunities for professional development and career advancement.
