Manager - Meanstack (AI)

Marsh; Dadri; Hybrid; INR 4,000,000 - 7,000,000; Full time

Job summary

Marsh is seeking a Senior Principal Engineer – Applications Development (Level E) to shape engineering strategy, drive enterprise-scale architecture decisions, and lead design, development, testing, and modernization of scalable platforms. This role requires hands-on depth, architectural judgment, and the ability to influence across multiple teams and products.

You will guide technical direction, design secure, resilient applications, advance CI/CD, and embed AI/GenAI capabilities across the

Qualifications

• Bachelor’s degree or higher in CS/Engineering or equivalent.

Responsibilities

• Provide technical leadership and architectural direction across multiple applications and streams.
• Design scalable, secure, and maintainable enterprise applications aligned with standards.
• Lead the engineering lifecycle from design to production optimization.
• Drive adoption of modern engineering practices including microservices and cloud-native engineering.
• Define comprehensive testing strategies across unit, integration, and performance testing.
• Establish engineering standards and reusable frameworks across teams.
• Develop high-quality automated testing frameworks and quality gates.
• Collaborate with DevOps to strengthen CI/CD pipelines and IaC.
• Embed security-by-design and remediate vulnerabilities across the stack.
• Mentor engineers and influence technology choices across squads.
• Explore AI/GenAI capabilities within SDLC and accelerate delivery.

Skills

Technical leadership

Architecture

Full-stack engineering

Cloud-native

Domain-driven design

Event-driven architecture

API-first design

Microservices

CI/CD

Security engineering

Testing strategies

Observability

AI, ML, GenAI

Leadership mentoring

Education

BTech / MCA preferred

Bachelor’s degree in Computer Science, Engineering, or equivalent practical experience

Tools

Angular

Node.js

Express.js

MEAN / MERN stack

Less / Sass

GitHub Actions

Docker

Kubernetes

Confluence

JIRA

GitHub

AWS

Azure

Job description

Work Model: Hybrid — at least three days a week in the office

What can you expect?

As a Senior Principal Engineer – Applications Development (Level E) at Marsh, you will serve as a senior technical leader responsible for shaping engineering strategy, driving enterprise-scale architecture decisions, and leading the design, development, testing, and modernization of highly scalable, resilient, and secure software platforms. This role requires deep hands-on expertise, strong architectural judgment, and the ability to influence engineering direction across multiple teams, products, and platforms.

We will count on you to:

• Provide technical leadership and architectural direction across multiple applications, products, or engineering streams.
• Design and deliver scalable, resilient, secure, and maintainable enterprise applications aligned with domain and enterprise architecture standards.
• Lead the engineering lifecycle from solution design through development, testing, deployment, observability, and production optimization.
• Drive adoption of modern engineering practices, including domain-driven design, event-driven architecture, API-first design, microservices, cloud-native engineering, and platform automation.
• Define and implement comprehensive quality engineering strategies, including unit, integration, contract, end-to-end, performance, security, and resiliency testing.
• Establish and evolve engineering standards, reusable frameworks, reference implementations, and best practices across teams.
• Lead the development of high-quality automated testing frameworks and quality gates to improve release confidence and reduce regression risk.
• Partner with DevOps and platform engineering teams to strengthen CI/CD pipelines, Infrastructure as Code, release governance, and environment automation.
• Embed security-by-design principles into engineering practices, including proactive remediation of SAST, DAST, dependency, secrets, and container vulnerabilities.
• Drive modernization and optimization initiatives for legacy applications, development toolchains, deployment pipelines, and runtime architectures.
• Mentor senior and junior engineers, fostering a culture of technical excellence, innovation, accountability, and continuous improvement.
• Influence and guide teams on technology choices, platform strategy, engineering trade-offs, and implementation approaches.
• Collaborate with stakeholders to break down complex business requirements into robust, scalable technical solutions.
• Analyze production issues, system bottlenecks, and test failures, and lead root cause analysis and long-term corrective actions.
• Stay current with emerging trends in software engineering, cloud platforms, developer tooling, AI engineering, and intelligent automation, and evaluate their practical adoption.

AI and Intelligent Engineering Responsibilities in SDLC

• As a Level E engineering leader, you will be expected to actively leverage and promote AI, ML, and Generative AI capabilities across the SDLC, including:
• Driving adoption of AI-assisted software development for code generation, code review, refactoring, documentation, and developer productivity acceleration.
• Applying Generative AI in test engineering, including automated test case generation, synthetic test data creation, intelligent test prioritization, defect prediction, and self-healing test automation.
• Using AI tools to improve requirements analysis, story refinement, impact assessment, and traceability across business and technical artifacts.
• Incorporating AI-driven approaches for application observability, anomaly detection, incident triage, root cause analysis, and operational optimization.
• Defining engineering patterns for integrating LLMs, SLMs, agentic AI systems, RAG architectures, prompt orchestration, vector stores, and AI workflow frameworks into enterprise applications.
• Ensuring responsible implementation of AI solutions through governance, security, privacy, explainability, bias awareness, model evaluation, and compliance controls.
• Identifying opportunities to embed AI into developer platforms, enterprise workflows, business processes, and customer-facing solutions.
• Leading engineering teams in the safe and scalable use of AI accelerators across design, build, test, release, and support functions.

What you need to have:

• Proven experience operating at a senior engineering leadership level, delivering complex enterprise applications across multiple teams, products, and platforms.
• Strong experience in full-stack software engineering, solution architecture, quality engineering, and enterprise delivery.
• Proven ability to lead technical implementation across a broad mix of languages, frameworks, platforms, and cloud environments.
• Strong communication and stakeholder management skills, with the ability to influence both technical and non-technical audiences.
• Deep experience with Agile, Lean, DevSecOps, Continuous Integration, Continuous Delivery, Test-Driven Development, and Infrastructure as Code.
• Proven experience with cloud-native architectures, distributed systems, asynchronous/event-driven patterns, and modern integration approaches.
• Strong experience in secure software engineering and remediation of vulnerabilities identified via SAST, DAST, open-source dependency scanning, and runtime/container security checks.
• Experience driving engineering quality through CI/CD, automation, policy controls, code quality gates, and release governance.
• Strong leadership capability as a self-starter, technical mentor, and cross-functional engineering influencer.
• Demonstrated experience integrating or enabling AI/ML/Generative AI capabilities within engineering workflows, products, or enterprise solutions.

Technical Skills or Qualifications Required:

• Bachelor’s degree in Computer Science, Engineering, or equivalent practical experience; BTech / MCA preferred.
• Extensive experience as a Senior Engineer, Lead Engineer, Principal Engineer, or equivalent role, with strong expertise in software development, architecture, and test engineering.
• Strong hands-on experience with modern frameworks and technologies such as:
• Angular
• Node.js
• Express.js
• MEAN / MERN stack
• Less / Sass
• Deep expertise in unit testing, integration testing, API testing, end-to-end testing, performance testing,
• Strong experience in building and maintaining automated testing frameworks, reusable test utilities, and quality engineering accelerators.
• Expertise in CI/CD pipelines and engineering toolchains, including:
• GitHub Actions
• Docker
• Artifact and package management tools
• Strong experience with containerization, orchestration, and cloud deployment patterns using Docker and Kubernetes.
• Proven knowledge of application architecture, design patterns, refactoring, system design, secure coding, and engineering best practices.
• Strong experience with ORM frameworks, relational and NoSQL databases, and data access design, including:
• T-SQL
• MS SQL Server
• MongoDB
• NoSQL data modeling practices
• Strong knowledge of SDLC processes, engineering governance, and collaboration tooling, including:
• Confluence
• JIRA
• GitHub
• Experience designing, deploying, and supporting applications on AWS and Microsoft Azure.
• Strong analytical, troubleshooting, and problem-solving capabilities, including the ability to resolve highly complex production and engineering issues.

Advanced AI / GenAI Skills Required for Level E

• Strong knowledge of Generative AI, AI engineering, and applied machine learning concepts, including:
• Small Language Models (SLMs)
• Agentic AI
• Retrieval-Augmented Generation (RAG)
• Embeddings and vector databases
• Fine-tuning and model adaptation concepts
• NLP and semantic search
• Knowledge representation and reasoning
• AI orchestration frameworks
• Experience with AI/ML and GenAI ecosystems, tools, or libraries such as:
• TensorFlow
• PyTorch
• LangChain / LangGraph / LangFlow
• Semantic Kernel
• Vector databases and retrieval frameworks
• Experience designing or contributing to AI-enabled applications, copilots, intelligent assistants, knowledge search, summarization, workflow automation, or decision-support systems.
• Understanding of AI governance, model evaluation, responsible AI, prompt safety, security, privacy, and compliance controls.
• Ability to identify and implement AI use cases across the SDLC, including:
• AI-assisted coding
• Test generation and optimization
• Release risk prediction
• Incident summarization and operational support
• Familiarity with MLOps / LLMOps concepts, model lifecycle considerations, and enterprise AI platform integration.
• Basic to working knowledge of Databricks and AI/data engineering ecosystems is highly desirable.

What makes you stand out?

• BTech / MCA or equivalent advanced technical background.
• Strong expertise in AI/ML and Generative AI-enabled software delivery.
• Demonstrated success in building or scaling engineering platforms, reusable components, and quality engineering practices.
• Experience driving adoption of AI across the SDLC to improve developer productivity, software quality, and delivery outcomes.
• Strong knowledge of cloud, security, observability, automation, and platform engineering.

Ability to balance hands-on engineering depth with strategic technical leadership across multiple squads or domains