Manager - Meanstack (AI)

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 Mercer, 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 strong 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