Head of AI Engineering

About the Project Lead our AI engineering team to transfer systems for financial services. You’ll manage a team of AI architects and engineers while automating workflows with AI agents. We’re creating production‑ready AI solutions for banks, insurance companies, and investment firms—from customer support bots to document automation and productivity assistants. Objective & KPIs Develop PoCs in 2 weeks Develop production solutions in 2 months ≥ 50 % internal engineering workflows fully automated by autonomous AI agents (baseline FY‑2025 audit) ≥ 75 % code/component reuse across new projects Production model accuracy ≥ 90 %, latency < 5 s, Codacy grade upgraded from B → A Maintain 0.375‑0.5 FTE as billable hours allocation at the client’s projects Areas of Responsibility Talent & Capability Building Hire, onboard, and retain A‑player AI Architects and AI engineers Empower AI architects and engineers with clear decision rights, context, and AI‑native tooling so they can execute autonomously and at speed Implement a skills‑matrix and personalized growth plans; coach next‑generation tech leads Make decisions on promotion based on performance reviews anchored in objective contribution metrics Promote a culture of continuous learning (regular "Agentic AI dojo", conference sponsorships, internal certifications) Provide technical oversight through senior AI Architects across all client engagements; sign off on architecture and go‑live readiness while mentoring them to own delivery Staff projects with the right talent mix; optimize utilization of core team members Engineering Excellence & AI‑Native Quality Update, automate, and collect AI engineering health indicators – including solution accuracy, latency, model drift, cost efficiency, and code quality – via a fully instrumented MLOps telemetry stack (CI/CD, feature store, observability, drift alerts) Establish and iterate the AI‑native SDLC: LLM‑assisted coding & test generation, agentic design patterns, self‑healing pipelines, prompt‑ops, red‑teaming, security & compliance Orchestrate autonomous AI agents to automate internal engineering and business routines such as environment provisioning, compliance evidence capture, cost optimization, and status reporting Maintain reference architectures and reusable component libraries; achieve ≥75 % code reuse across all new work Convert learnings from services projects into IP that reduces future build effort by > 40 % Own the design, packaging, and optimization of Neurons Lab solutions Skills AI‑native software engineering & agentic architectures MLOps automation and observability Large‑scale AWS (SageMaker, Bedrock, EKS) optimization Regulatory & security compliance for FSI Organizational design and talent development KPI‑driven process improvement Strategic thinking & systems‑level problem‑solving Knowledge Core‑banking, insurance, and asset‑management data flows & systems LLM orchestration patterns and prompt engineering best practices Foundations of traditional machine learning and ML models training from scratch Financial‑services regulatory frameworks AWS Marketplace packaging and Advanced‑Tier Partner requirements Code‑quality measurement (e.g., Codacy) and secure SDLC principles Experience Led AI/ML engineering teams 15 → 50 + in FSI domain while maintaining velocity; delivered production agentic AI systems with ≥ 90 % accuracy & < 5 s latency Deployed autonomous AI agents that automated ≥ 40 % of engineering/business processes Established, maintained and improved engineering standards and quality measures Seniority level Director Employment type Full‑time Job function Engineering and Information Technology Industries IT Services and IT Consulting #J-18808-Ljbffr