Manager - Meanstack (AI)

Applications Development (Level D/E)\nLocation: Gurgaon / Noida\nWork Model: Hybrid — at least three days a week in the office\n\nWhat can you expect?\nAs 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.\n\nWe will count on you to:\nProvide technical leadership and architectural direction across multiple applications, products, or engineering streams.\nDesign and deliver scalable, resilient, secure, and maintainable enterprise applications aligned with domain and enterprise architecture standards.\nLead the engineering lifecycle from solution design through development, testing, deployment, observability, and production optimization .\nDrive adoption of modern engineering practices , including domain-driven design, event-driven architecture, API-first design, microservices, cloud-native engineering, and platform automation.\nDefine and implement comprehensive quality engineering strategies , including unit, integration, contract, end-to-end, performance, security, and resiliency testing.\nEstablish and evolve engineering standards, reusable frameworks, reference implementations, and best practices across teams.\nLead the development of high-quality automated testing frameworks and quality gates to improve release confidence and reduce regression risk.\nPartner with DevOps and platform engineering teams to strengthen CI/CD pipelines, Infrastructure as Code, release governance, and environment automation .\nEmbed security-by-design principles into engineering practices, including proactive remediation of SAST, DAST, dependency, secrets, and container vulnerabilities.\nDrive modernization and optimization initiatives for legacy applications, development toolchains, deployment pipelines, and runtime architectures .\nMentor senior and junior engineers, fostering a culture of technical excellence, innovation, accountability, and continuous improvement .\nInfluence and guide teams on technology choices, platform strategy, engineering trade-offs, and implementation approaches .\nCollaborate with stakeholders to break down complex business requirements into robust, scalable technical solutions.\nAnalyze production issues, system bottlenecks, and test failures, and lead root cause analysis and long-term corrective actions.\nStay current with emerging trends in software engineering, cloud platforms, developer tooling, AI engineering, and intelligent automation , and evaluate their practical adoption.\n\nAI and Intelligent Engineering Responsibilities in SDLC\nAs a Level E engineering leader, you will be expected to actively leverage and promote AI, ML, and Generative AI capabilities across the SDLC , including:\nDriving adoption of AI-assisted software development for code generation, code review, refactoring, documentation, and developer productivity acceleration.\nApplying Generative AI in test engineering , including automated test case generation, synthetic test data creation, intelligent test prioritization, defect prediction, and self-healing test automation.\nUsing AI tools to improve requirements analysis, story refinement, impact assessment, and traceability across business and technical artifacts.\nIncorporating AI-driven approaches for application observability, anomaly detection, incident triage, root cause analysis, and operational optimization .\nDefining engineering patterns for integrating LLMs, SLMs, agentic AI systems, RAG architectures, prompt orchestration, vector stores, and AI workflow frameworks into enterprise applications.\nEnsuring responsible implementation of AI solutions through governance, security, privacy, explainability, bias awareness, model evaluation, and compliance controls .\nIdentifying opportunities to embed AI into developer platforms, enterprise workflows, business processes, and customer-facing solutions.\nLeading engineering teams in the safe and scalable use of AI accelerators across design, build, test, release, and support functions .\n\nWhat you need to have:\nProven experience operating at a senior engineering leadership level , delivering complex enterprise applications across multiple teams, products, and platforms.\nStrong experience in full-stack software engineering, solution architecture, quality engineering, and enterprise delivery .\nProven ability to lead technical implementation across a broad mix of languages, frameworks, platforms, and cloud environments .\nStrong communication and stakeholder management skills, with the ability to influence both technical and non-technical audiences.\nDeep experience with Agile, Lean, DevSecOps, Continuous Integration, Continuous Delivery, Test-Driven Development, and Infrastructure as Code .\nProven experience with cloud-native architectures , distributed systems, asynchronous/event-driven patterns, and modern integration approaches.\nStrong experience in secure software engineering and remediation of vulnerabilities identified via SAST, DAST, open-source dependency scanning, and runtime/container security checks.\nExperience driving engineering quality through CI/CD, automation, policy controls, code quality gates, and release governance .\nStrong leadership capability as a self-starter, technical mentor, and cross-functional engineering influencer .\nDemonstrated experience integrating or enabling AI/ML/Generative AI capabilities within engineering workflows, products, or enterprise solutions.\n\nTechnical Skills or Qualifications Required:\nBachelor’s degree in Computer Science, Engineering, or equivalent practical experience; BTech / MCA preferred .\nExtensive experience as a Senior Engineer, Lead Engineer, Principal Engineer, or equivalent role , with strong expertise in software development, architecture, and test engineering.\nAdvanced proficiency in one or more programming languages such as JavaScript/TypeScript, C#, Python , with strong experience in enterprise-scale application delivery.\nStrong hands-on experience with modern frameworks and technologies such as:\nAngular\nNode.js\nExpress.js\n.NET / .NET Core\nMEAN / MERN stack\nLess / Sass\nDeep expertise in unit testing, integration testing, API testing, end-to-end testing, performance testing,\nStrong experience in building and maintaining automated testing frameworks , reusable test utilities, and quality engineering accelerators.\nExpertise in CI/CD pipelines and engineering toolchains , including:\nAzure DevOps\nGitHub Actions\nDocker\nKubernetes\nArtifact and package management tools\nStrong experience with containerization, orchestration, and cloud deployment patterns using Docker and Kubernetes.\nProven knowledge of application architecture, design patterns, refactoring, system design, secure coding, and engineering best practices .\nStrong experience with ORM frameworks , relational and NoSQL databases, and data access design, including:\nT-SQL\nMS SQL Server\nMongoDB\nNoSQL data modeling practices\nStrong knowledge of SDLC processes, engineering governance, and collaboration tooling, including:\nConfluence\nJIRA\nAzure DevOps\nGitHub\nExperience designing, deploying, and supporting applications on AWS and Microsoft Azure .\nStrong analytical, troubleshooting, and problem-solving capabilities, including the ability to resolve highly complex production and engineering issues.\n\nAdvanced AI / GenAI Skills Required for Level E\nStrong knowledge of Generative AI, AI engineering, and applied machine learning concepts , including:\nLarge Language Models (LLMs)\nSmall Language Models (SLMs)\nAgentic AI\nPrompt engineering\nRetrieval-Augmented Generation (RAG)\nEmbeddings and vector databases\nFine-tuning and model adaptation concepts\nNLP and semantic search\nKnowledge representation and reasoning\nAI orchestration frameworks\nExperience with AI/ML and GenAI ecosystems, tools, or libraries such as:\nTensorFlow\nPyTorch\nLangChain / LangGraph / LangFlow\nSemantic Kernel\nHugging Face\nOpenAI / Azure OpenAI patterns\nVector databases and retrieval frameworks\nExperience designing or contributing to AI-enabled applications , copilots, intelligent assistants, knowledge search, summarization, workflow automation, or decision-support systems.\nUnderstanding of AI governance, model evaluation, responsible AI, prompt safety, security, privacy, and compliance controls .\nAbility to identify and implement AI use cases across the SDLC, including:\nAI-assisted coding\nAutomated documentation generation\nIntelligent code review\nTest generation and optimization\nDefect triage\nRelease risk prediction\nIncident summarization and operational support\nFamiliarity with MLOps / LLMOps concepts , model lifecycle considerations, and enterprise AI platform integration.\nBasic to working knowledge of Databricks and AI/data engineering ecosystems is highly desirable.\n\nWhat makes you stand out?\nBTech / MCA or equivalent advanced technical background.\nProven experience leading enterprise-scale engineering modernization initiatives.\nStrong expertise in AI/ML and Generative AI-enabled software delivery .\nDemonstrated success in building or scaling engineering platforms, reusable components, and quality engineering practices .\nExperience driving adoption of AI across the SDLC to improve developer productivity, software quality, and delivery outcomes.\nStrong knowledge of cloud, security, observability, automation, and platform engineering .\nAbility to balance hands-on engineering depth with strategic technical leadership across multiple squads or domains