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Manager – Software Engineering (Full Stack, AI-First)

Location: Bangalore, India (Hybrid)
Experience: 10–15 years

Role Type: Hybrid – Hands-on + People Leadership
Domain: Healthcare Technology (Oncology / Clinical Platforms)

We are looking for a Manager of Software Engineering to lead high-impact product and platform engineering teams building scalable healthcare applications.

This is a hybrid leadership role requiring:

• Strong technical depth in full-stack development (Java + Spring Boot + React)

• Hands-on involvement in architecture and engineering decisions

• Proven people leadership — hiring, mentoring, and scaling high-performing teams

You will also lead teams in an AI-first engineering culture, using tools like GitHub Copilot and Windsurf to improve developer productivity without compromising quality, maintainability, or security.

Key Responsibilities

Engineering Leadership (Core Responsibility)

• Lead and grow a team of 5–8 engineers across backend and frontend

• Drive delivery of high-quality, production-grade software aligned with business goals

Own team execution:

• Sprint commitments

• Risk management

• Delivery predictability

Establish strong engineering culture:

• Accountability

• Ownership

• Clean code practices

• Design discipline

Technical Leadership (Non-Negotiable)

Actively participate in:

• System design and architecture decisions

• API design and microservices standards

• Code reviews for critical components

Guide teams on:

• Java + Spring Boot microservices architecture

• Event-driven systems (Kafka or equivalent)

• Scalable frontend design using React + TypeScript

Ensure systems meet expectations for:

• Scalability

• Performance

• Security (especially PHI/PII handling)

• Reliability

This is not a purely managerial role. Ability to deep-dive into code and design when required is essential.

AI-First Engineering Leadership

Drive adoption of AI tools (Copilot, Windsurf) to:

• Improve engineering velocity

• Reduce repetitive work

• Accelerate testing and documentation

Establish guardrails for AI usage:

• No blind copy-paste from AI-generated code

• Mandatory validation for correctness, security, and performance

Encourage:

• AI-assisted testing and debugging

• Building observable systems (logs, metrics, traces) for better diagnostics

Talent Management

• Hire strong engineers in a competitive market

• Build a high-performance culture

Mentor engineers across levels:

• Technical growth (design, coding, debugging)

• Career progression

• Ownership mindset

Handle:

• Performance management

• Low performance correction

• Offshore/onshore collaboration

Delivery & Execution Excellence

Own end-to-end delivery across:

• Backend services

• APIs

• UI integrations

• Data flows

Enforce:

• CI/CD discipline

• Testing rigor (unit + integration)

• Release quality standards

Partner with:

• Product Managers

• QA teams

• DevOps / Platform teams

Minimum Requirement

Must-Have Skills

Technical

• 10–15 years of software engineering experience

• Strong hands-on experience with:

  • Java + Spring Boot (microservices, REST APIs)

  • React.js + TypeScript

Experience with:

• Distributed systems / microservices architecture

• SQL databases (Oracle/Postgres)

• Event-driven systems (Kafka or similar)

• Strong debugging and production issue handling skills

Leadership

• 3–5 years of engineering management experience

Proven track record of:

• Hiring strong engineers

• Managing and mentoring teams

• Delivering complex projects

AI-First Expectations (Non-Negotiable)

Candidates must demonstrate:

• Practical use of AI coding assistants in development workflows

• Ability to review, validate, and production-harden AI-generated code

• Strong engineering discipline in an AI-assisted environment

Good-to-Have Skills

• Healthcare domain experience

Familiarity with:

• FHIR / HL7 / C-CDA standards

• HIPAA compliance and PHI/PII handling

Experience with:

• Databricks / data pipelines

• Elasticsearch / Redis

• Docker / Kubernetes

What Success Looks Like

• Consistent delivery of high-quality, production-ready features

• Strong engineering culture with high ownership and low defect leakage

• Effective use of AI to improve speed without lowering standards

Systems built are:

• Scalable

• Observable

• Reusable

Why Join Us

• Build healthcare systems impacting patient outcomes

• Work on modern architecture with AI-driven engineering workflows

• Opportunity to shape engineering practices at scale