CTO (Chief Technology Officer): React + Python/NodeJS + AI (INR 70-100 LPA)

We're hiring for one of our clients operating at the intersection of healthcare + AI.

Responsibilities:

(a) Engineering Visibility & Cross-Domain Co-ordination:

• Design, build, and scale the end-to-end AI/Healthcare platform architecture from agentic frameworks (LangGraph, ADK) to production infrastructure (AWS, Docker, CI/CD).
• Own the technical roadmap: prioritize agent development sprints, manage cross-agent dependencies, and make architecture decisions that balance speed with healthcare- grade reliability.
• Define inter-agent communication flows, A2A task schemas, and the architectural standards that every agent (including those built by the COOs team) must follow.

Set and enforce engineering standards across the entire agent ecosystem: code quality, testing coverage, documentation, security, and HIPAA compliance at the code level.

(b) Team Building and 100X Engineering Culture:

• Hire, mentor, and retain a high-performance engineering team that meets the 100x engineer standard: AI-first builders who work in Claude Code, own their agents end-to- end, and ship production code in weekly sprints.
• Build and operate the Agentic Engineering Framework the automated system that tracks every engineers progress through Claude Code session logs, GitHub commits, Linear tickets, and CI/CD pipeline data.
• Publish the engineering leaderboard twice weekly: every engineer measured on velocity, quality, collaboration, and innovation. Scores are reviewed at Saturday integrated reviews.
• Drive the Amazon-style memo culture engineers submit written memos (max 2 pages) every Friday. You set the standard for technical writing quality and architectural clarity.
• Onboard new engineers into the architecture through structured training: core document mastery, CLAUDE.md templates, Claude Code proficiency, and agent-specific deep dives.

(c) Production Reliability & Healthcare Compliance:

• Own infrastructure reliability, scalability, and security. Your systems handle PHI (Protected Health Information) HIPAA compliance, encryption, audit trails, and role- based access control are non-negotiable.
• Oversee the deployment pipeline: CI/CD best practices, automated testing, staging environments that mirror production, and zero-downtime deployments to live medical practices.
• Establish and monitor SLIs/SLOs for every agent in production: uptime, latency, accuracy, and error rates. Build observability systems (Grafana, logging, alerting) that surface issues before they impact patient care.
• Lead technical compliance implementation: HIPAA/HITRUST at the infrastructure level, SOC2 readiness, security audits, and data encryption standards.

(d) Engineering Architecture and Agent Development:

• Build and operate the CTO Command Dashboard the hub of the spoke-and-hub architecture that gives you real-time visibility into every agents development status, every engineers output, and every infrastructure metric.
• Present the 15-minute engineering segment at every Saturday integrated review. Your segment covers: agents on track vs. behind, deployment frequency, critical blockers, and engineering team health.
• Incorporate production feedback from deployed practices into sprint priorities. When a physician reports a Scribe Agent issue at a California practice, it flows through your system and reaches the right agent lead within hours.
• Provide architectural guidance and infrastructure support to the COOs four operational agent leads.Their agents run on your platform and must meet your engineering standards.

Tech stack:

• Agent Frameworks: LangGraph, Google ADK (Agent Development Kit), A2A protocol, MCP Servers, Claude Code
• Backend: Python, FastAPI, Node.js
• Frontend: React, Next.js, TypeScript, Tailwind CSS
• Data Layer: PostgreSQL, Redis, MongoDB, MerCore Foundry (proprietary medical data schemas)
• Healthcare Integrations: FHIR/HL7, EHR systems, payer portals, clearinghouses
• Infrastructure: AWS, Docker, Kubernetes, GitHub Actions, Terraform
• Observability: Grafana, Swarmia, Linear, automated Claude Code session logging
• AI/ML: LLM orchestration, model serving, embedding models, vector databases

Skills & Qualifications:

• 6-10+ years of hands-on software engineering experience, with at least 3+ years in a tech lead, principal engineer, or engineering leadership role. FAANG-level or top-tier product company experience preferred.
• Proven track record of building and scaling production systems from zero to one. You have taken a product from architecture whiteboard to live users and managed the engineering team that shipped it.
• Deep system design expertise: distributed systems architecture, data pipeline design, build-vs-buy trade-offs, and the ability to evaluate latency, cost, accuracy, and reliability trade-offs under pressure.
• Strong backend engineering fundamentals: Python or Node.js, API design, database schema design, queue systems, caching strategies, and production debugging.
• Working knowledge of AI/ML systems: LLMs, agentic frameworks, prompt engineering, model serving, and the practical challenges of deploying AI in production. You dont need to train models, but you need to understand what makes them work and fail.
• Experience hiring, mentoring, and scaling engineering teams (5-20+ engineers). You set technical standards, run code reviews, conduct architecture reviews, and build a culture of ownership and velocity.
• Excellent written and verbal communication. You write architecture docs, present to the CEO, and make complex engineering decisions legible to non-technical leadership.

Nice-to-have:

• Experience building or contributing to a YC-style startup or high-growth product environment where you wore multiple hats and shipped under pressure.
• Healthcare domain experience: EHR integrations (FHIR/HL7), revenue cycle management systems, clinical workflows, HIPAA compliance, or healthcare data interoperability.
• Experience with agentic AI architectures: LangGraph, LangChain, multi-agent orchestration, tool-use patterns, or agent-to-agent communication protocols.
• Familiarity with Claude Code or AI-assisted engineering workflows as a daily development tool.
• Experience with infrastructure-as-code (Terraform), container orchestration (Kubernetes), and cloud cost optimization at scale.