Lead end-to-end delivery of AI/ML and GenAI solutions for enterprise customers across industries such as financial services.
Own delivery execution across multiple concurrent AI workstreams, ensuring scope, timelines, quality and business outcomes are met.
Manage, mentor and grow a team of AI engineers and data scientists, owning hiring, onboarding, performance management and career development.
Own staffing and capacity planning across engagements, matching skills to accounts and clearing blockers for the team.
Manage technical delivery risks, dependencies and coordination across engineering, data science, product and customer teams.
Partner with customers to translate business problems into technical AI solutions, including model design, architecture and deployment approach.
Stay hands-on: prototype, review and debug code, and make and defend architecture decisions.
Guide design and review of ML models (predictive ML, time series, classification, clustering) and GenAI systems (LLMs, SLMs, RAG, prompt engineering, evaluation, agentic workflows).
Ensure best practices across the ML lifecycle: data preparation, feature engineering, training, evaluation, deployment, monitoring and governance.
Raise the technical bar across the team through design and code reviews, pairing and structured feedback.
Act as the primary technical point of contact and escalation point for customers, balancing delivery execution with stakeholder management from engineer to executive level.
Partner with sales and pre-sales on scoping, estimation, statements of work and expansion opportunities.
Collaborate with product and engineering teams to build reusable accelerators and reference architectures, and feed field learnings back into the roadmap.
Responsibilities
Lead end-to-end delivery of AI/ML and GenAI solutions for enterprise customers across industries such as financial services.
Own delivery execution across multiple concurrent AI workstreams, ensuring scope, timelines, quality and business outcomes are met.
Manage, mentor and grow a team of AI engineers and data scientists, owning hiring, onboarding, performance management and career development.
Own staffing and capacity planning across engagements, matching skills to accounts and clearing blockers for the team.
Manage technical delivery risks, dependencies and coordination across engineering, data science, product and customer teams.
Partner with customers to translate business problems into technical AI solutions, including model design, architecture and deployment approach.
Stay hands-on: prototype, review and debug code, and make and defend architecture decisions.
Guide design and review of ML models (predictive ML, time series, classification, clustering) and GenAI systems (LLMs, SLMs, RAG, prompt engineering, evaluation, agentic workflows).
Ensure best practices across the ML lifecycle: data preparation, feature engineering, training, evaluation, deployment, monitoring and governance.
Raise the technical bar across the team through design and code reviews, pairing and structured feedback.
Act as the primary technical point of contact and escalation point for customers, balancing delivery execution with stakeholder management from engineer to executive level.
Partner with sales and pre-sales on scoping, estimation, statements of work and expansion opportunities.
Collaborate with product and engineering teams to build reusable accelerators and reference architectures, and feed field learnings back into the roadmap.
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