With over 25 years as an engineering services provider, we believe in the power of doing things differently to make the impossible possible. We bring together technologies and industries, alongside the contributions of diverse individuals who are empowered by an intentional workplace culture, to solve problems better and faster.

As Engineering Lead for our P&DI; data products, you'll own the technical strategy behind our AI-enabled insights-first platforms — and the team that builds them. Set the technical strategy, architecture, and roadmap across data pipelines, ML services, the agent layer, and the governance layer (PII masking, audit trace, guard-railed execution) — and push reusable, scalable components across every deployment.

A hands-on engineer with the ability to independently design, develop, deploy, and maintain scalable software solution that includes backend services, APIs, and microservices to support AI-driven applications.

Build and deploy cloud-native applications using Docker, Kubernetes, and contemporary cloud infrastructure.

Develop full-stack applications, including responsive web interfaces, backend APIs, and integration services.

Integrate existing AI models, LLM services, and agentic frameworks into enterprise applications.

Implement and manage Kubernetes workloads, including deployment, scaling, networking, service discovery, and resource optimization.

Build and maintain CI/CD pipelines and automate infrastructure provisioning, application deployment, and operational workflows.

Establish monitoring, logging, distributed tracing, and alerting to ensure application reliability and performance.

Identify and resolve performance bottlenecks, infrastructure issues, and production incidents.

Collaborate with architects, Data Scientists, AI engineers,



and product teams to translate technical requirements into production-ready solutions.

Contribute to system architecture, technical documentation, code reviews, and engineering best practices.

Drive responsible AI through governance, security, model evaluation, risk management, and compliance with customer and enterprise standards.

Architect for deployment inside customers' own networks — zero dependency on third-party AI services — with resiliency and business continuity built in.

Mentor engineers and technical leads, raising the bar on AI literacy and technical excellence.

Act as a strategic thought partner to business and technology leaders — challenge the status quo, and reset priorities as the landscape shifts.

A bachelor's degree from a reputed university; 10–12 years hands-on experience in software development, cloud engineering, or platform engineering, including 3–4+ years leading a team or technical track.

Strong programming skills in Python, with experience in frameworks such as FastAPI, Flask, or Django.

Experience with backend development, RESTful APIs, microservices, and asynchronous programming.

Proficiency in JavaScript/TypeScript and frontend frameworks such as React or Next.js with component libraries such as Tailwind CSS.

Strong hands-on experience with Docker and Kubernetes, including container orchestration, deployment, networking, and scaling.





Understanding of GPU infrastructure, resource scheduling, and AI inference workload management.

A track record designing, building, and running enterprise-scale applications — production infrastructure, deployment architectures (on-prem, private cloud, or AWS/GCP/Azure), resiliency, and business continuity.

Experience with infrastructure automation and deployment tools such as Terraform, Helm, and CI/CD pipelines.

Experience implementing authentication and authorization using OAuth 2.0, OpenID Connect, JWT, or similar standards. Also, experience with identity and access management platforms such as Keycloak.

Familiarity with Generative AI applications, LLM APIs, and AI inference services. Exposure to agentic AI architectures, tool calling, and the Model Context Protocol (MCP).

Experience with relational databases such as PostgreSQL or MySQL, Clickhouse, along with exposure to NoSQL databases and vector databases.

Familiarity with observability and monitoring tools such as Prometheus, Grafana, OpenTelemetry, or ELK Stack.

Strong understanding of Linux environments, networking fundamentals, Git, and software development best practices.

Grounding in Responsible AI principles, governance frameworks, and model risk management.

We believe that our company culture is the key to our ability to make a true difference in every industry we reach. We wholeheartedly believe in the diversity of thought that comes with fostering a culture rooted in respect, where everyone belongs, is valued, and feels inspired to share their ideas. We know embracing our unique differences makes us better, and that solving the worlds hardest engineering problems requires diverse ideas, perspectives, and backgrounds.

📌 Engineering Tech Lead (Frontend) (Mumbai)
🏢 Quest Global
📍 Mumbai