Top Tech Jobs & Startup Jobs

Lead technical presales activities for data, software, analytics, and AI solutions. Conduct customer discovery, translate business and scientific needs into use cases and architectures, deliver tailored demonstrations, develop proposals, and support proofs of concept and pilots. Advise customers on LLMs, RAG, agents, knowledge graphs, and responsible AI while collaborating with Sales, Product, Professional Services, and Marketing teams to ensure feasible, scalable solutions.

Lead complex EHR integration discussions with healthcare customers, translating technical requirements into suitable clinical solutions. Manage access and entitlement workflows, support configuration and implementation, document integration best practices, answer technical questions, and improve processes that enhance customer satisfaction and retention. Collaborate with implementation leads, customer IT teams, and internal departments across solutions involving HL7, FHIR, OAuth, SAML, SMART on FHIR, and single sign-on.

Build and operate production MLOps, NLP, search, recommendation, GenAI, RAG, and agentic AI systems. Develop cloud-based ML pipelines, CI/CD, model registries, evaluation frameworks, retrieval infrastructure, and governance controls. Work with Elasticsearch, vector and graph databases while optimizing reliability, scalability, cost, and search quality. Collaborate with data scientists, product managers, subject-matter experts, Responsible AI teams, and operations engineers to deliver secure AI services.

Develop and enhance trade and commercial reporting solutions using Snowflake, Tableau, and related BI tools. Modernize manual and legacy reporting workflows into automated dashboards and scalable analytical solutions. Translate queries and business rules across data platforms, validate reporting accuracy, investigate discrepancies, document workflows, and partner with commercial and senior stakeholders to deliver actionable insights.

Lead and develop a data science team building production AI systems using machine learning, retrieval, ranking, and large language models. Set technical strategy, oversee experimentation and deployment, establish evaluation and responsible AI practices, manage team capacity, recruit talent, and partner with product and engineering. Guide scalable, reliable solutions from problem framing through monitoring and iteration while communicating trade-offs and business impact to technical and non-technical stakeholders.

Provides technical leadership across data science teams developing AI, NLP, machine learning, generative AI, LLM, RAG, retrieval, ranking, and recommendation solutions. Establishes architecture, engineering standards, evaluation and monitoring frameworks, governance, and compliance practices. Mentors teams, conducts technical reviews, guides high-risk implementations, and represents data science with senior stakeholders. The role requires strong Python and machine learning expertise, experience with text-rich datasets and search systems, and the ability to influence technical strategy across multiple teams.

Develop and evaluate applied AI capabilities for Elsevier’s LeapSpace research assistant. Responsibilities include building LLM-powered workflows, RAG and search pipelines, agentic systems, evaluation frameworks, experimentation methods, and semantic discovery tools. The role requires collaboration with engineering, product, UX, and scientific domain experts to productionize trustworthy AI solutions.

Build and ship production-grade AI features, including RAG pipelines, tool-augmented agents, and agentic workflows. Integrate AI capabilities into enterprise systems, contribute to evaluation, observability, guardrails, and outcome measurement, and maintain reusable technical patterns. Collaborate with stakeholders to ensure solutions fit real workflows, identify risks, complete bug fixes, document implementations, and support handover and capability-building.

Design, develop, and lead scalable generative AI services and reusable platform components using Python and Java. Drive architecture, engineering standards, distributed systems design, Kubernetes-based deployment, observability, testing, and CI/CD. Collaborate with product, platform, and research teams to productionize AI prototypes, resolve complex technical challenges, mentor engineers, and promote responsible AI integration.

Develop and deploy scalable document intelligence services that transform scientific manuscripts into structured data using distributed systems, microservices, event-driven architecture, AWS, data science, and AI/LLM capabilities. Responsibilities include architecture, implementation, deployment, observability, operational support, continuous improvement, stakeholder collaboration, Agile and DevOps practices, mentoring, and improving automated content processing and editorial experiences.