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
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 &
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
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