This description is a summary of our understanding of the job description. Click on 'Apply' button to find out more. Role Description Hex is an AI-powered platform for modern Data Science and Data Analytics workflows. AI Engineers at Hex partner with product teams to build industry-leading AI experiences such as the Notebook Agent. In the process, AI Engineers will run experiments, fine-tune models, deploy AI infrastructure, and build and maintain experimentation tooling. The backbone of all of our AI experiences is providing relevant context to the agent. As an AI Engineer focused on our search and context architecture, you’ll be responsible for building out key components of our agentic platform, including: - Agentic search and discovery subagents - High-scale, permissions-aware indexing systems If you’re a strong builder excited to bring these capabilities to thousands of users, come do it on the best Data Science platform around, with the best user context. We are looking for a senior engineer (from an AI Eng, SWE or MLE background) eager to rapidly expand our capabilities in several greenfield applications. As one of our team’s early hires, you will work on a variety of initiatives including: - Experimenting with new agentic techniques for search, discovery, and context management - Designing and implementing the architecture for our scalable search and indexing pipelines - Working at the cutting edge of production AI applications deployed to real customers This type of ownership will provide incredible opportunities for personal and professional growth. You will have ample technical and team leadership opportunities, depending on your interests. We aim to deliver AI capabilities that radically accelerate and augment the data science workflow – we’ve got an exciting roadmap and are willing to share more details during the process. We’re particularly interested in candidates who bring inspired excitement about what is possible in this space. Note: We deeply respect and appreciate deep AI/ML research; this role is not that. While experience producing relevant journal publications is awesome, it won’t be part of this role’s deliverables. Qualifications - Experience building and measuring high quality search and recommendation systems - Experience getting AI/ML capabilities into production and serving real users - A lot of enthusiasm for applications of AI to real business problems - Understanding of core MLOps/SW Architecture concepts for modern ML-based applications - You’re comfortable working in both Python & JS/TS – it’s okay if you’re only strong in one, but openness to both is important - Experimentalist mindset – understanding that quick iteration, and discerning critical thinking, can yield rapid progress on new challenges - Interest in the data space, and a love of shipping great products and building tools that empower end users to do more - Experience maintaining a high quality bar for design, correctness, and testing Benefits - Competitive total rewards package, including market-benched salary & equity - Comprehensive health benefits - Flexible paid time off Salary Range The salary range for this role is: $225,000-$285,000. The salary range shown may be a reflection of additional factors such as geographical location and skill ranges/levels we’re open to. Placement in the salary range will be decided upon completion of the interview process, taking into account factors like leaving room for growth, internal fairness & parity, your demonstrated skills, and the depth of your experience. Our Recruiting team will be able to provide more details during the interview process. By submitting an application the candidate consents to the use of their personal information in accordance with the Hex Privacy policy: Hex Privacy Policy .

• Design, develop, and deploy AI-driven applications and services, with a strong focus on Machine Learning and Generative AI use cases. • Build and integrate LLM-based solutions using frameworks such as LangChain, ensuring reliability, scalability, and performance. • Collaborate with product managers, designers, and data teams to translate business requirements into technical AI solutions. • Implement data pipelines, model training workflows, and inference services across different environments. • Optimize models and systems for performance, cost, and scalability in production. • Ensure best practices related to model evaluation, monitoring, versioning, and responsible AI usage. • Contribute to technical discussions, architectural decisions, and proof-of-concepts for AI initiatives. • Stay up to date with emerging AI technologies, tools, and industry trends.

• Define and lead end-to-end AI architecture for complex ecosystems, balancing technical depth with business outcomes. • Translate business strategy into scalable AI technology solutions through discovery workshops, assessments, and roadmaps. • Act as a technical sponsor for CI&T’s most strategic accounts, working closely with C-level clients and non-technical stakeholders. • Lead architectural design, technology selection, and proof-of-concepts for critical AI platforms and innovation programs. • Govern technical quality across delivery squads, driving adherence to security, performance, scalability, and privacy standards. • Support pre-sales, proposal development, RFPs, and technical visioning with account teams and clients. • Coach and mentor senior architects and technical leads, shaping technical career development paths.

• Train, fine-tune, evaluate, and improve NLP, speech-to-text, and LLM-based models used in production environments • Work hands-on with chatbots, summarisation, and language understanding features, including retrieval-augmented generation (RAG) and vector-based retrieval systems • Design and run model evaluations, benchmarking existing approaches and validating improvements before deployment • Read, assess, and experiment with relevant AI/ML research and emerging techniques, translating promising ideas into practical, production-ready solutions • Contribute to prompt design, model optimisation, and iterative experimentation to improve accuracy, latency, and reliability of deployed models • Integrate models into existing backend services using Python-based APIs, collaborating closely with backend engineers • Ensure models are production-ready, maintainable, and resilient when deployed in live customer-facing systems • Support investigation and resolution of AI-related production issues in collaboration with engineering and platform teams • Work closely with engineering teams to align AI capabilities with product requirements and platform constraints • Communicate progress, trade-offs, and technical decisions clearly in planning and delivery discussions