Top Tech Jobs & Startup Jobs

Leads technical execution for an embedded AI innovation squad, taking prototypes from discovery through production. Designs reusable agentic AI architectures covering orchestration, tool use, memory, retrieval, integrations, security, and governance. Works with stakeholders to define requirements, makes architecture decisions, develops reference implementations, and establishes measurable outcomes. Provides infrastructure and budget recommendations, resolves complex technical issues, manages delivery dependencies, serves as the primary AI and coding expert, and mentors engineers while evaluating emerging technologies.

Design, build, and operate scalable backend integration services and APIs. Configure and manage Kong API Gateway via declarative YAML and CI/CD. Implement FHIR data facades, backend integrations (REST/gRPC/messaging), OpenAPI-driven lifecycle, automated testing, and secure service-to-service auth (OAuth2/OIDC). Deploy and operate containerized cloud services with observability and reliability patterns.

Leads AI and data science strategy across machine learning, NLP, search, recommendation, experimentation, and generative AI. Manages technical teams, develops talent, and oversees AI systems from experimentation through production. Builds LLM, RAG, semantic search, and knowledge-discovery solutions; establishes evaluation frameworks, quality metrics, experimentation practices, and responsible AI standards. Partners with product, technology, and business leaders to shape roadmaps and deliver measurable customer and business outcomes.

Lead strategy and execution for a SaaS product portfolio in Academic and Government Solutions. Define vision, roadmap, and KPIs; drive discovery, research, data-driven decisions, go-to-market, and commercial outcomes. Build and develop a high-performing product management team and collaborate cross-functionally with engineering, data science, UX, sales, and customer success to maximise customer value and business growth.

Leads enterprise strategy planning, portfolio roadmaps, growth initiatives, and cross-functional strategic projects for Elsevier. Partners with senior leaders and teams across Finance, Product, Technology, Marketing, Sales, HR, and Corporate Affairs. Develops executive and Board-level presentations, conducts market and data analysis, gathers customer insights, makes strategic recommendations, and tracks progress against objectives and metrics.

Join an embedded innovation squad to design, prototype, and productionize agentic AI solutions. Build RAG and retrieval pipelines, integrate AI into enterprise workflows, implement observability and metrics, apply CI/CD and best practices, mentor junior engineers, and produce reusable patterns and documentation.

Design, build, test, and deploy cloud-hosted distributed web applications as part of an Agile cross-functional team. Contribute to architecture, improve engineering practices and delivery pipelines, work with stakeholders to refine requirements, and apply test-driven development and mocking to deliver scalable, user-focused solutions.

Hands-on automation-first quality engineer focused on API and service-level testing for search and AI platform services. Design, implement, and maintain automated test suites (Karate, Cucumber, RestAssured) in Java; validate functional and non-functional behavior; integrate tests into CI/CD; use observability tools (New Relic, logging) for diagnosis; contribute to contract testing, incident reviews, and shaping acceptance criteria with engineering teams.

Design, build, and evaluate LLM-powered research workflows, retrieval and RAG systems, and AI evaluation frameworks for LeapSpace. Develop search, ranking, embeddings, and agentic workflows; run experiments, build benchmarks, and collaborate cross-functionally to deploy production AI features.

Lead development and evaluation of large-scale search, retrieval, and RAG systems. Design retrieval architectures, experiments, and evaluation frameworks; build evaluation datasets; integrate domain ontologies and structured biomedical data; collaborate with engineering and product to deploy scalable, production-ready ML/NLP solutions and drive responsible AI practices.