Lead Product Development AI Engineer

CI/CD pipelines (GitHub Actions, Jenkins, Azure DevOps)

Vector databases (Azure AI Search, Pinecone, Weaviate, FAISS)

Responsible AI (prompt injection defense, PII masking, policy enforcement)

Experimentation (A/B testing, feature flags – Split.io)

Platform thinking (shared AI services, reusable components)

Product Development

Lead technical design of features; partner with Product, BA, UX and architects to derive solution architecture, technical specifications, and delivery plans.

Define and document non-functional requirements (latency, availability, security, scalability, observability) and ensure features meet agreed targets.

Design and build microservices and RESTful APIs using Java 11/17 and Spring Boot; apply clean architecture principles, API versioning, backward compatibility, and secure-by-default patterns.

Lead frontend architecture and develop in Angular 18+ including state management, modular design, performance optimization, accessibility, and consistent UX patterns.

Drive engineering excellence: code reviews, operational readiness (logging, metrics, tracing, alerting).

Own end-to-end delivery for assigned epics—from design through implementation, rollout, monitoring, and iterative improvements—while proactively managing risks and dependencies.

AI Development

Implement AI integrations, including RAG patterns, embeddings, prompt orchestration, and prompt safety/guardrails.

Build AI assistants and agent-based workflows embedded into product experiences, including tool/function calling, multi-step reasoning flows, and user feedback loops.

Write and maintain high-quality prompts and system instructions; apply prompt engineering best practices, structured outputs, and robust error handling.

Design retrieval pipelines: chunking strategies, embedding selection, indexing approaches, and relevance tuning; choose and integrate vector databases as appropriate.

Establish evaluation (AI eval) practices: define success metrics, build offline/online evaluation datasets, implement regression tests, and monitor quality drift in production.

Ensure responsible AI practices: data privacy, PII handling, prompt injection mitigation, content safety, and secure integration of model endpoints.

Optimize AI feature performance with respect to latency and cost, batching, streaming responses, fallback strategies, and graceful degradation.

Collaboration

Mentor and guide engineers on full-stack and AI integration patterns; raise overall engineering standards through examples and coaching.

Communicate trade-offs clearly to stakeholders; drive alignment on scope, milestones, quality bars, and risk mitigation.

Contribute to technical roadmap and platform improvements (developer experience, shared libraries, observability, security posture).

Our team develop the product Cortellis and we develop features and enhancements on Cortellis umbrella of products like CCI, CDI, CTI and CRI

Cortellis is Clarivate’s flagship product in Life Sciences domain. Cortellis customers include top 50 global pharmaceutical companies, amongst many others. The technology team for Cortellis sits out of Bengaluru and consists of four different squads that work on development of new features using latest technologies. Our tech stack includes Angular, NodeJS, Java, Spring Boot, Oracle, Postgres, Elastic Search, Coherence etc. We use microservices architecture and cloud-native tools/technologies that include EC2, S3, SQS etc.

Full-time contract (9 hours per day, including 1 hour break, IST hours)

At Clarivate, we are committed to providing equal employment opportunities for all qualified persons with respect to hiring, compensation, promotion, training, and other terms, conditions, and privileges of employment. We comply with applicable laws and regulations governing non-discrimination in all locations.