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Open nowPosted 55 days ago

Senior Engineering Manager

Workable (global search)107,990 open roles

Where
Bengaluru, KA, India
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Your applicationOpen nowSenior Engineering ManagerWorkable (global search) · Bengaluru, KA, India

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The clock on this job

Early applications get read.

7.9% of postings close within 7 days. Measured by our own scanner across the market. Workable (global search) postings stay open a median of 6 days.

Share of postings closed within
  1. 1.6%1 day
  2. 3.6%3 days
  3. 7.9%7 days
  4. 14.9%14 days
  5. 34.0%30 days
This job: posted 55 days ago

Workable (global search) median: 6 days open

The posting

This role is for one of the Weekday's clients

Salary range: Rs 6500000 - Rs 9000000 (ie INR 65 - 90 LPA)

Min Experience: 11+ years

Location: Bengaluru JobType: full-time

Interview Availability: Candidates should be available for an in-person interview at the Bangalore office on 12th / 13th / 14th.

Role Summary

We are looking for an experienced Manager Engineering – Backend who combines strong engineering leadership with deep hands-on technical expertise. The ideal candidate will lead from the codebase, taking ownership of architecture, production systems, engineering quality, and team development rather than operating solely through meetings and delegation.

You will lead and grow a high-performing backend engineering team while remaining technically involved in designing and developing scalable, reliable backend systems. The role will also involve integrating Generative AI and Agentic AI capabilities into modern enterprise products, with a strong focus on production-grade engineering, distributed systems, APIs, and cloud-native architectures.

Requirements

Key Responsibilities

Backend Architecture & Technical Leadership

  • Design and develop scalable, highly available, and maintainable backend systems and services.
  • Own end-to-end backend architecture, including APIs, microservices, data flows, integrations, and production infrastructure.
  • Make pragmatic technology and architecture decisions based on scalability, reliability, performance, security, and maintainability.
  • Remain hands-on with production development and contribute code to critical systems.
  • Conduct detailed code and architecture reviews and establish strong engineering standards.
  • Identify and prioritize technical debt, performance improvements, reliability initiatives, and engineering risks.
  • Drive system design, API design, distributed systems architecture, and production engineering practices.

GenAI & Agentic AI Integration

  • Lead the development and integration of Generative AI and Agentic AI capabilities into backend products and workflows.
  • Design and implement LLM-powered services, AI APIs, RAG pipelines, and intelligent automation systems.
  • Work with engineering and product teams to determine appropriate approaches such as API-based models, open-source models, RAG, fine-tuning, or hybrid architectures.
  • Design reliable agentic systems incorporating tool calling, orchestration, context management, memory, guardrails, and evaluation mechanisms.
  • Build backend infrastructure that supports AI-powered features reliably at production scale.
  • Establish monitoring, evaluation, observability, and reliability mechanisms for AI-powered services.

Team Leadership & Development

  • Lead and mentor a team of backend engineers while maintaining strong technical involvement.
  • Build a high-performing engineering organization through hiring, onboarding, mentoring, and performance development.
  • Define engineering goals, ownership areas, technical standards, and delivery expectations.
  • Conduct architecture deep-dives, technical mentoring sessions, code reviews, and collaborative problem-solving.
  • Encourage a culture of engineering excellence, experimentation, accountability, and continuous improvement.
  • Maintain delivery velocity while ensuring adequate focus on engineering quality, reliability, and innovation.
  • Partner with Product, Design, Data, QA, and other stakeholders to translate business requirements into scalable technical solutions.

Delivery & Production Ownership

  • Own the complete engineering lifecycle from system design and development to deployment, monitoring, maintenance, and optimization.
  • Establish effective CI/CD, automated testing, observability, and production support practices.
  • Ensure backend systems meet performance, scalability, availability, and security requirements.
  • Proactively identify production issues and lead root-cause analysis and resolution.
  • Drive continuous improvements to engineering processes, development velocity, and system reliability.

Requirements

Must-Have Skills

  • 10–14 years of software engineering experience, with significant experience building and operating backend systems.
  • 3+ years of engineering management or technical leadership experience with demonstrated team ownership.
  • Strong hands-on programming experience in Java or a comparable backend technology.
  • Strong understanding of backend architecture, system design, APIs, microservices, and distributed systems.
  • Experience designing and deploying production-grade cloud-native applications.
  • Current hands-on coding experience with a willingness to contribute directly to critical production systems.
  • Strong experience with CI/CD, automated testing, monitoring, observability, and production operations.
  • Experience working with Generative AI, LLMs, or Agentic AI in production environments.
  • Understanding of RAG architectures, prompt engineering, LLM integrations, tool calling, and AI application development.
  • Strong technical decision-making and problem-solving capabilities.
  • Proven ability to hire, mentor, and develop high-performing engineering teams.

Good-to-Have Skills

  • Experience with GenAI / Generative AI platforms and frameworks.
  • Experience building AI-powered enterprise applications.
  • Familiarity with RAG pipelines, LLM orchestration, evaluation frameworks, and AI observability.
  • Experience with cloud platforms such as AWS, GCP, or Azure.
  • Experience with containerization and orchestration technologies such as Docker and Kubernetes.
  • Exposure to ML infrastructure, model serving, feature stores, experiment tracking, or A/B testing.
  • Experience in automotive, ERP, fintech, enterprise SaaS, or other complex domain platforms.
  • Familiarity with responsible AI practices including hallucination mitigation, data privacy, security, and auditability.
  • Contributions to open-source projects or experience working on applied AI/ML initiatives.

Educational Qualifications

  • Bachelor's or Master's degree in Computer Science, Software Engineering, Information Technology, or a related technical discipline.
  • Equivalent professional experience with a strong track record in backend engineering and technical leadership will also be considered.

Ideal Candidate Profile

The ideal candidate is a hands-on engineering manager who enjoys solving complex backend problems and is comfortable balancing technical execution with people leadership. You should be able to move seamlessly between writing production code, designing systems, reviewing architecture, mentoring engineers, hiring talent, and collaborating with senior stakeholders.

You should have a strong interest in emerging GenAI and Agentic AI technologies and the ability to translate them into reliable, scalable production capabilities rather than experimental prototypes alone.

Key Success Metrics

Success in this role will be measured through backend system reliability, scalability, engineering velocity, quality of production releases, technical debt reduction, team growth and retention, successful delivery of AI-powered capabilities, and the ability to build a strong engineering culture.

The role will also be responsible for maintaining high engineering standards while ensuring that backend and AI capabilities are delivered efficiently, securely, and at production scale.

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