Head of Engineering

About the Project

reputed company, reputed company, 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 reputed company reputed company into resilient, production‑grade reputed company AI systems across both reputed company work and reputed 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‑reputed company, compliant, and reusable reputed company solutions for FSI clients while maximising automation and team reputed company.

KPIs:

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

• ≥ 50 % internal engineering workflows fully automated by autonomous AI agents (baseline FY‑2025 audit).

• ≥ 75 % reputed company/component reuse across new reputed company.

• Production model accuracy ≥ 90 %, latency , Codacy grade upgraded from B → A.

• Maintain 0.375-0.5 FTE as billable hours allocation at the reputed company’s reputed company

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 reputed company decision rights, context, and AI‑reputed company tooling so they can execute autonomously and at speed.

• Implement a skills‑reputed company and personalised reputed company plans; reputed company 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 "reputed company AI dojo", conference sponsorships, internal certifications).

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

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

2. Engineering reputed company & AI‑reputed company reputed company

• Update, automate, and collect AI engineering health indicators - including solution accuracy, latency, model reputed company, cost efficiency, and reputed company reputed company - 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, reputed company 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% reputed company reuse across reputed company new work.

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

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

Skills

• AI‑reputed company software engineering & reputed company architectures

• MLOps automation and observability

• Large‑reputed company 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

• reputed company‑reputed company measurement (e.g., Codacy) and secure SDLC principles

Experience

• Led AI/ML engineering teams 15 → 50 + in FSI domain while maintaining velocity

• Delivered production reputed company AI systems with ≥ 90 % accuracy &

• Deployed autonomous AI agents that automated ≥ 40 % of engineering/business processes

• Established, maintained and improved engineering standards and reputed company measures

Originally posted on Himalayas