Role Overview: This is a hands-on technical leadership role. You will be a part of team that builds and scales proprietary tools that automate the full primary research / survey workflow — from selecting approaches and panels, through fielding and validation, to analysis and visualization of survey data.

The broader group supports private equity work with specialized analytics products (survey analytics, digital diagnostics, workforce analytics, disruption assessment, etc.) and combines deep domain expertise with modern technology, including generative and agentic AI plus heavy data analytics.

Experience: 8-10 years for Manager and 10-13 years for Sr. Manager

Key Responsibility:

- Full ownership of architecture, quality, delivery speed, reliability, security, scalability, maintainability, and technical alignment across the portfolio.
- Defining the long-term technical vision and driving convergence toward shared infrastructure, common components, and consistent engineering practices.
- Guiding product-level engineering leads on architecture decisions, resolving cross-product dependencies and trade-offs, and ensuring strong execution.
- Building shared platform capabilities: authentication & authorization, hosting & deployment, CI/CD, observability, security scanning, API standards, shared data layers, reusable components, and cross-product integrations.
- Owning the portfolio’s security and data-governance approach (access control, data sharing, compliance, and responsible AI use).
- Staying hands-on in critical technical areas — designing and implementing shared components, APIs, integrations, production data and ML pipelines, asynchronous processing, and performance-sensitive infrastructure; contributing to individual product codebases when needed.
- Setting and maintaining engineering standards (architecture, code quality,



automated testing, performance, observability, documentation, release management, production reliability).
- Shaping the architecture for AI/LLM capabilities across the products (RAG, semantic search, copilots and agents, enterprise model gateways, LLMOps, evaluation, and monitoring).
- Translating product and business needs into technical roadmaps and implementation plans, balancing user impact and timelines with scalability, security, quality, and cost; proactively surfacing risks, dependencies, and trade-offs.
- Building engineering capability: mentoring leads and developers, conducting architecture and code reviews, supporting technical hiring, evaluating new technologies, and encouraging reuse and knowledge sharing.

This is a hands-on leadership role — primary focus is engineering leadership across the portfolio, while remaining close to the technology on the most important shared and critical work.

Required Technical Skills

- Strong full-stack engineering experience:
- Server-side: FastAPI, Django, Flask, Node.js or similar.
- Client-side: React, Angular or Vue.js, plus solid HTML/CSS.
- Designing and building RESTful APIs and microservices; solid understanding of API design best practices; working knowledge of relational and/or NoSQL databases, data modeling, schema design, and performance optimization.
- Proven experience designing shared/platform infrastructure (common authentication, CI/CD, shared data layers)



that multiple independent product teams build on.
- Hands-on experience designing and deploying cloud-native applications on AWS, Azure or GCP; expertise in containerization and orchestration (Docker, Kubernetes); Infrastructure as Code (Terraform, CloudFormation, AWS CDK or similar).
- DevOps and CI/CD experience (GitHub Actions, GitLab CI, Azure DevOps or similar), including automated testing and deployment pipelines; experience defining and improving engineering processes (agile practices, estimation, release management).
- Experience owning security and data governance posture at an organizational or platform level (access control, cross-service data sharing, compliance) — beyond a single product.
- Exposure to or interest in AI/LLM-based systems (RAG, semantic search, copilots), including LLMOps tooling (prompt/instruction versioning, model gateways such as Portkey, inference frameworks like LangChain or LlamaIndex, response caching).
- Model evaluation and monitoring: golden datasets, metric definition, calibration, LLM-as-judge, regression gates in CI, and production drift monitoring.

Experience, Soft Skills & Education

- 8+ years of professional software engineering experience, with significant work on web/cloud applications and demonstrated growth into platform or architecture-level scope.
- 4+ years in a Staff/Principal Engineer, Architect, or platform-lead role setting technical direction and standards across multiple teams or products (formal people management not required, but a clear track record of driving technical alignment across teams is essential).
- Robust code review and technical mentorship skills; high ownership, curiosity, and pragmatism — comfortable balancing engineering excellence with delivery timelines.

📌 Manager – Full Stack (Python, Node, AI) (Gurugram)
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📍 Gurugram