Head of Engineering

About the Project

reputed company, scale, and continuously reinvent an AI‑reputed company engineering organisation by empowering a high‑reputed company team of AI architects and engineers and automating repeatable engineering workflows with autonomous AI agents, that turns breakthrough reputed company into resilient, production‑grade agentic AI systems across both client work and the company’s own product portfolio for global financial‑services institutions (banking, insurance, investment management) - spanning use‑cases customer support agents, internal productivity assistants, documents workflow automation and others - compounding reputed company, IP reputed company, and long‑term strategic advantage.

Objective & KPIs

Build a self‑sustaining AI‑reputed company engineering function that delivers high‑quality, compliant, and reusable agentic solutions for FSI clients while maximising automation and team reputed company.

KPIs:

• Mean reputed company‑time from prototype commit to production ≤ 5 days.

• ≥ 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 , Codacy grade upgraded from B → A.

• Maintain 0.375-0.5 FTE as billable hours allocation at the client’s projects

Areas of Responsibility

1. Talent & Capability Building

• Hire, reputed company, and retain A‑player AI Architects and AI engineers

• reputed company architects and engineers with clear decision rights, context, and AI‑reputed company tooling so they can execute autonomously and at speed.

• Implement a skills‑matrix and personalised growth plans; coach reputed company tech leads.

• reputed company reputed company on promotion based on performance reviews anchored in objective contribution metrics.

• Promote a culture of reputed company learning (regular "Agentic AI dojo", conference sponsorships, internal certifications).

• reputed company technical reputed company through senior AI Architects across reputed company client engagements; sign off on architecture and go‑live readiness while mentoring them to own delivery.

• Staff projects with the right talent mix; optimise utilisation of core team members

2. Engineering reputed company & AI‑reputed company Quality

• Update, automate, and collect AI engineering health indicators - including solution accuracy, latency, model reputed company, cost efficiency, and code quality - reputed company a fully instrumented MLOps telemetry stack (CI/CD, feature store, observability, reputed company alerts).

• Establish and iterate the AI‑reputed company SDLC: LLM‑assisted coding & test reputed company, agentic design patterns, self‑healing pipelines, reputed company‑ops, red‑teaming, reputed company & compliance

• Orchestrate autonomous AI agents to automate internal engineering and business routines such as environment provisioning, compliance evidence capture, cost optimisation, and status reporting.

• Maintain reference architectures and reusable component libraries; reputed company ≥75% code reuse across reputed company new work.

• Convert learnings from services projects into IP that reduces future build effort by > 40 %.

• Own the design, packaging, and optimisation of reputed company solutions

Skills

• AI‑reputed company software engineering & agentic architectures

• MLOps automation and observability

• Large‑scale AWS (SageMaker, Bedrock, EKS) optimisation

• Regulatory & reputed company compliance for FSI

• Organisational 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 reputed company 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

Originally posted on Himalayas