gravity9

• Own and prioritize the product backlog aligned to business outcomes • Translate needs into clear user stories and acceptance criteria • Partner with engineering to deliver incremental, high-quality value • Lead Agile ceremonies and enable team effectiveness • Drive decisions and manage tradeoffs across value, risk, and timelines • Build strong understanding of customer needs and business goals • Navigate a broad, complex stakeholder landscape across business and IT • Communicate priorities, progress, and risks with clarity and consistency • Coordinate across teams and dependencies to ensure end-to-end delivery

• Delivery and technical leadership • Lead the architecture and hands-on implementation of end-to-end ML systems: data ingestion, pipelines, feature stores, training, evaluation, serving, and monitoring • Own technical decisions across the full stack, data platform, training environment, model serving, and MLOps tooling • Set engineering standards for ML projects: experiment tracking, model versioning, reproducibility, governance, observability, drift monitoring, and CI/CD for ML • Coach and uplift other engineers on the team in modern ML and MLOps practices • Stay accountable for quality, security, and operational soundness of what we ship • Pre-Sales and pipeline support • Partner with the sales leadership team across pre-sales activity: discovery calls, scoping workshops, technical briefings, and LOE preparation • Lead architecture and solutioning conversations with prospects and customers, translate business problems into credible, defensible technical approaches • Provide dedicated technical support to opportunities flowing through the partners sales process, including positioning their products as part of broader data and AI architectures, joint solutioning sessions, and partner-aligned proposals • Contribute to thought leadership and demand generation: blog posts, webinars, capability decks, conference talks, and reference architectures

• Lead the architecture and hands-on implementation of end-to-end ML systems • Own technical decisions across the full stack • Set engineering standards for ML projects • Coach and uplift other engineers on the team • Partner with the sales leadership team across pre-sales activity • Lead architecture and solutioning conversations with prospects and customers • Provide dedicated technical support to opportunities flowing through the partners sales process • Contribute to thought leadership and demand generation

• Day-to-day helpdesk administration • Identity and Access Management • Endpoint security and compliance • SaaS administration • Internal tooling and automation development • Light DevOps support

• Engage with clients to understand their unique challenges and deliver tailored AI/ML solutions • Design, implement, and maintain scalable, efficient, and robust end-to-end ML/AI systems • Manage the entire ML/AI development lifecycle, including planning, pipeline management, and deployment • Provide technical support to engineering team members, promoting best practices in ML engineering • Collaborate with AI Practice Lead to inform and shape the company's AI strategic direction

• Design and implementation of secure and scalable applications • Participate in team discussions • Collaborate with colleagues to identify, prioritise and deliver features that bring value to clients • Work in small, multi-disciplinary teams to deliver complex change

• Design and implement secure and scalable applications that are transforming industries • Collaborate with colleagues to identify, prioritise and deliver value-adding features • Participate in team discussions and code reviews

• Interaction with stakeholders, ensuring expectations are managed and business goals are understood • Accountable for the design of the solutions • Overseeing and managing development teams • At least 50% hands-on coding in Java, Node.js, OR Python

• Design the Attribute-Based Access Control (ABAC) model, including policy attributes (clearance level, nationality, group/role, context), enforcement points, and decision logic • Design the RESTful API architecture, including endpoint structure, request/response contracts, and error handling patterns • Map out query patterns, access flows, and integration touchpoints with external systems (data storage and encryption services provided by partner organisations) • Collaborate with the database consulting engineer on schema design, search strategy, and encryption approach to ensure the API layer aligns with the underlying data model • Contribute to Infrastructure as Code design for deployment (Terraform or similar) • Build a RESTful API layer in Java implementing full catalogue functionality: – CRUD operations for metadata records – Advanced search capability leveraging Atlas Search – ABAC enforcement on all API operations, ensuring search results and data retrieval are filtered by the requestor’s access attributes – Secure storage and retrieval of encrypted key material (shard keys) via CS-FLE • Implement unit tests to validate functional correctness • Build automated data load and stress testing harnesses for the combined API and cluster deployment • Perform integration testing against external partner systems (or appropriate stand-ins) to validate end-to-end data creation and retrieval flows • Produce API documentation and integration guides for consuming systems • Support knowledge transfer and handover activities in the final weeks of the engagement

• Interaction with stakeholders, ensuring expectations are managed and business goals are understood • Accountable for the design of the solutions • Overseeing and managing development teams • At least 50% hands-on coding in Java, Node.js, OR Python