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Senior, niche ML/LLM plus full-stack skills reduce candidate pool despite known employer and metro location.
Strong ML/LLM, MLOps, and enterprise SaaS experience required, moderately transferable across industries.
Many mandatory skills, explicit 10+ years, and security/compliance needs make shortlisting highly selective.
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Lead the design, build, and operation of core ML/AI systems across Customer Success & Support workflows impacting CSAT, deflection, MTTR, and agent productivity.
Own end-to-end AI system lifecycle from architecture, model training, deployment, MLOps, to observability and safety/governance controls for generative AI systems.
Develop scalable full-stack solutions integrating AI components with platform systems and cloud services; mentor engineers and set engineering standards.
10+ years software engineering experience with significant recent experience shipping ML/AI systems to production.
6+ years building full-stack systems using React.js, Next.js, TypeScript, JavaScript, Node.js, Python, Go, and Java.
Hands-on experience with AWS and Azure cloud services, CI/CD tools (GitHub Actions, Azure DevOps, Jenkins), and Infrastructure as Code (Terraform, Bicep, ARM).
Bachelor’s or advanced degree in Computer Science, Machine Learning, or related field, or equivalent experience.
Experienced in delivering scalable, production-grade AI/ML systems with strong focus on operational efficiency, safety, and measurable business impact in customer success/support contexts.
Strong applied ML/NLP expertise including working knowledge of LLMs, retrieval-augmented generation, prompt engineering, and MLOps for model lifecycle management.
Senior technical leader skilled at translating customer success product requirements into robust AI architectures and collaborating cross-functionally with product, security, and support teams.