Applications Development (Level D/E)

Location: Gurgaon / Noida

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.
- Advanced proficiency in one or more programming languages such as JavaScript/TypeScript, C#, Python, with robust experience in enterprise-scale application delivery.
- Strong hands-on experience with modern frameworks and technologies such as:
- Angular
- Node.js
- Express.js
- .NET / .NET Core
- 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:
- Azure DevOps
- GitHub Actions
- Docker
- Kubernetes
- 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
- Azure DevOps
- 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:
- Large Language Models (LLMs)
- Small Language Models (SLMs)
- Agentic AI
- Prompt engineering
- 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
- Hugging Face
- OpenAI / Azure OpenAI patterns
- 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
- Automated documentation generation
- Intelligent code review
- Test generation and optimization
- Defect triage
- 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.
- Proven experience leading enterprise-scale engineering modernization initiatives.
- 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

📌 Manager - Meanstack (AI) (Noida)
🏢 Marsh
📍 Noida