Description
About ASAP Talent Services - a leading executive search firm focusing on ERP, SAP, Cybersecurity, AI/ML and modern IT/Engineering roles.
About our client: a fast-growing late-stage Pre-IPO SaaS with $50M+ ARR and over $500M raised through multiple rounds of funding. PE's like Blackrock are invested and they have major customers already including the likes of Nike, Walmart, Petsmart, and Fabletics!
Location: Hybrid role in The Bay Area - ideal for those closer to Menlo Park or Redwood City.
Reports to: Head of Product
Seeking an AI Solutions Lead to identify, design, and operationalize AI-Powered use cases across retail and supply chain clients. This is a builder/operator role - ideal for someone who can spot business problems, rapidly prototype AI/ML solutions, and drive them to production like a product owner & engineer combined!
You'll work directly with clients, engineers, and data scientists to turn retail challenges into deployable AI features - at startup speed, but enterprise scale.
If your ideal day involves brainstorming use cases, protyping models, collaborating with engineering, and launching new AI-powered capabilities in weeks (not months), this role is for you.
Key Responsibilities:
- Own end-to-end discovery, design, and delivery of AI/ML-powered features and microservices across SaaS and robotics platforms.
- Identify AI use-cases in collaboration with clients and internal teams; rapidly evaluate business impact and technical feasibility.
- Prototype AI/ML solutions for key retail challenges (inventory accuracy, task optimization, demand forecasting, associate assistance, etc)
- Build and manage a pipeline of AI "micro-solutions" from proof-of-concept to A/B pilots to production rollouts.
- Partner with engineering and product teams to integrate AI features into existing SaaS products or develop standalone services
- Serve as technical product owner for AI/ML initiatives, ensuring models are not just built but operationalized at scale
- Continuously scan retail trends and AI advancements to inform new feature development
Qualifications:
- 5+ years in AI/ML solution development, applied data science, or technical product delivery
- Demonstrated experience building and deploying AI/ML-powered features or microservices in real-world production systems
- Strengths: ML pipelines (TensorFlow, PyTorch, Vertex AI, etc), LLMs, Computer Vision, and/or time-series forecasting; Cloud-native environments (Google Cloud, etc); APIs, Microservices, and distributed systems.
- Ability to quicky assess and prioritize AI use cases based on business impact and technical effort
- Comfort working with both technical engineering teams and business stakeholders.
- Experience in SaaS, Retail, Supply Chain, or IoT-driven industries
- B.S. in Computer Science, Engineering or related field - Masters Degree preferred. Dual degrees like Mathematics could be interesting
Why This Role?
- You'll build AI solutions that touch real stores and real supply chains - not just theoretical models.
- You'll work in an environment that values speed, experimentation and autonomy
- You'll drive AI Strategy while staying hands-on in solution development
- You'll have direct client exposure and influence over product direction
What Your First 90 Days Could Look Like?
- Scoping & Prioritizing AI Use Cases
- Partnering with Product, Engineering, and Client Teams to surface the highest value retail problems where AI can unlock real associate and operational productivity. A focus on fast, tangible solutions over theoretical models.
- Launching a Virtual Store Associate MVP - a first buidl could be releasing a Virtual Store Associate - leveraging data and AI models (likely LLM-driven) to help store associates query inventory, receive task prompts, and get operational assistance in real time.
- Whether as a mobile app assistant or API-enabled chat service, you'd own both the architecture and initial pilot to launch!
- Operationalizing AI Fast - this role is about output. We'd expect you to work as both Architect and Builder - rapidly pushing prototypes into controlled pilots and scaling them into production environments with Engineering Support.
