• Lead discovery sessions and SME alignment workshops; produce structured requirements and integration assumption documentation • Define data ingestion scope: folder selection, corpus boundaries, PII classification requirements, and tenant governance rules • Translate expert-weighting business logic into specifications for the retrieval and ranking layer • Author and maintain the accuracy rubric framework used for SME validation • Write and validate user stories (INVEST-compliant) with acceptance criteria tied to delivery milestones • Produce and maintain schema documentation for the data processing pipeline • Facilitate query clustering analysis to prioritise accuracy tuning work • Bridge between client Designated Expert input and the engineering team's implementation backlog • Maintain requirements traceability across Jira and Confluence throughout all phases

Role Description We’re looking for a highly motivated and visionary Full-Stack Engineer with deep expertise in Artificial Intelligence to join our core engineering team. In this role, you will be instrumental in advancing our flagship platforms like Intellego® XT, Discovery, and LocationWise. You will design, build, and deploy complex end-to-end distributed systems and AI-powered applications that process massive volumes of multi-modal investigative data. Working at the intersection of full-stack engineering and AI, you will build next-generation web interfaces, geospatial intelligence tools, and generative AI pipelines that help law enforcement turn complex data into actionable clarity. What You'll Work On - Developing complex, end-to-end distributed web applications on cloud-native platforms, primarily utilizing AWS. - Integrating and deploying LLM and deep learning-based applications, including voice analytics (such as OpenAI Whisper), document analysis, and image/video intelligence (e.g., artifact detection) using open-source models. - Building interactive, LLM-driven frontends and complex UI applications involving dynamic dashboards and precise mapping/geolocation for our location intelligence products. - Designing scalable, high-throughput backend architectures utilizing Kubernetes, Kafka, Redis and distributed data stores. - Utilizing AI-assisted coding tools (like Claude Code, Codex, or Devin) to dramatically accelerate development, code reviews, and automated testing cycles. - Collaborating with cross-functional teams to engineer data platforms that fuse network intelligence, open-source intelligence, and high-precision location data into a "single pane of glass." Key Responsibilities - Architect, implement, and maintain high-performance full-stack applications using React (and related ecosystems) for the frontend and robust distributed technologies for the backend. - Design, implement and troubleshoot microservices within Kubernetes-based environments, ensuring high availability and fault tolerance. - Develop and optimize scalable data processing pipelines utilizing distributed systems theory, caching (Redis), search engines (OpenSearch), event streaming (Kafka), and diverse database architectures (Graph, Relational, Document, Multimodal). - Drive the operationalization of Deep Learning and Generative AI models into production environments to augment human analysts. - Implement secure, hardened access controls via robust OAuth and Active Directory (AD) integrations to maintain evidentiary integrity and auditability. - Lead by example in modern software engineering practices, leveraging AI coding assistants for enhanced productivity and mentoring peers. Required Experience & Qualifications - 5+ years of professional full-stack development experience building complex, large-scale distributed systems. Familiarity with Java, Python and JavaScript/Typescript based development. - Extensive hands-on experience with cloud-native platforms such as AWS (preferred), GCP, or Azure. - Proficiency in Kubernetes-based orchestration, deployment, and operational troubleshooting. - Solid engineering background in distributed systems and state management (Kafka, Redis, OpenSearch, and various multi-modal databases). - Deep expertise in frontend development using React.js, with a proven track record of delivering complex UIs (e.g., data-heavy dashboards, mapping, and geo-location products). Must Have - Verifiable experience building and deploying Deep Learning and Generative AI solutions (e.g., LLM-based UIs, voice/image/video analytics using open-source models). - Practical experience incorporating AI-assisted coding tools (Claude Code, GitHub Copilot/Codex, Devin) into your daily engineering workflow. - Strong understanding of enterprise security concepts, specifically OAuth and AD integration. Nice to Have - Hands-on DevOps, CI/CD, and infrastructure-as-code deployment experience, specifically within AWS. - Additional background in large-scale big data platform development, data crawling and data engineering/processing. - Previous experience in lawful intercept, telecommunications data (5G, IMSI/IMEI tracking), cybersecurity analytics, or digital forensics. - Proficiency with Golang and Rust. Compensation & Benefits - The expected base salary range for this position is $110,000 - $135,000. Actual compensation will be determined based on the candidate’s skills, experience, and qualifications. - This role is also eligible to participate in SS8’s corporate bonus program, subject to the terms of the applicable plan. - We offer a comprehensive benefits package including medical, dental, vision, 401(k) with company match, and paid time off. Equal Opportunity Employer SS8 is an Equal Opportunity Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, national origin, sex, gender identity or expression, sexual orientation, age, disability, protected veteran status, or any other status protected by applicable law. SS8 does not accept unsolicited resumes from staffing agencies, search firms, or third parties. Any resumes submitted without a signed agreement in place will be considered the property of Company, and no fees will be paid if a candidate is hired as a result.

Role Description As Staff AI Engineer, you are the technical leader and backbone of Cadre's Colombia engineering team. Your job is to ensure every engineer around you builds better. You mentor and manage engineers, set the standard for how the team designs, reviews, and ships AI systems, and own their growth as deliberately as you own your own code. You pair that leadership with serious technical depth, staying hands-on with the hardest builds in our client portfolio: - Multi-step agent systems - Complex document processing pipelines - Automations that touch core revenue operations When a problem has ambiguous requirements or no obvious solution, the team looks to you, and clients measure the result in EBITDA impact, not demos. You report to the Head of Client Engineering and occasionally join client calls to walk stakeholders through what we are building and why. You thrive here if leveling up the people around you energizes you as much as shipping the system yourself. What You'll Do - Team Leadership and Mentorship - Manage and mentor engineers on the Colombia team, owning their technical growth, code quality, and delivery. - Set the engineering bar through rigorous code review, pairing, and reference implementations. - Serve as the first escalation point when engineers hit problems they cannot solve alone, teaching as you unblock. - Build the habits, patterns, and practices that make every engineer on the team measurably better quarter over quarter. - Technical Direction - Drive architecture decisions across client builds: agentic workflows, RAG pipelines, document processing, and automation running in production. - Evaluate new models, frameworks, and techniques as they emerge, deciding what enters Cadre's production toolkit. - Turn hard-won lessons from client builds into reusable internal tooling, templates, and documentation. - Build and Ship Production AI Systems - Stay hands-on with Cadre's most complex client systems, owning delivery end to end. - Make pragmatic build decisions under real constraints: client timelines, legacy systems, messy data, and measurable business outcomes. - Ship fast without sacrificing reliability; design systems that keep working long after the engagement ends. - Client Collaboration - Join client calls when the conversation needs technical depth, walking stakeholders through system designs, trade-offs, and progress. - Translate ambiguous business problems into concrete system designs and delivery plans clients can commit to. - Flag risks, trade-offs, and scope realities directly and early, protecting both the client outcome and the team's delivery. Qualifications - 8+ years of software engineering experience, including 2+ years building LLM-powered systems that run in production. - Demonstrated technical leadership: engineers you have mentored or managed improved due to your guidance. - Expert-level Python and strong full-stack fundamentals. - Production cloud experience (AWS, GCP, or Azure). - Deep working knowledge of the modern AI stack. - A track record of owning ambiguous, high-stakes projects end to end. - Fluent English and clear communication on client calls. - Consulting-environment adaptability. - Based in Colombia with reliable overlap with US business hours. - An AI-first working style. Benefits - Real leadership: be the technical backbone of a growing engineering team. - AI-native culture: work with people who are as obsessed with the tools as you are. - Access to the frontier: experiment with new models and capabilities through partnerships. - Upside: share in the success of a bootstrapped, profitable, and fast-growing company. - No bureaucracy: small teams with clear accountability.

• Design, develop, and deploy enterprise AI applications, AI agents, copilots, and intelligent workflow solutions across manufacturing and corporate domains. • Build and maintain AI orchestration pipelines utilizing LLMs, retrieval-augmented generation (RAG), vector search, prompt frameworks, and agent-based architectures. • Develop scalable AI integration patterns between enterprise platforms such as Snowflake, Salesforce, ERP systems, SCADA/OT systems, document repositories, and collaboration platforms. • Collaborate with data scientists and business stakeholders to operationalize machine learning and generative AI solutions into production environments. • Design APIs, middleware, and integration services supporting AI-driven automation and cross platform communication. • Develop and optimize semantic search, enterprise knowledge retrieval, and conversational AI capabilities. • Support the implementation of AI governance, security, observability, model evaluation, and human-in-the-loop approval processes. • Drive enterprise AI architecture strategy and standards, partnering across teams to define approaches for AI platforms, orchestration, deployment, and lifecycle management.