Role Description This role is about engineers who build the AI features our users actually interact with — chat interfaces, agents, retrieval systems, AI-powered workflows inside our product. We're hiring a full stack engineer who can ship LLM-powered features end-to-end: from the prompt and the eval suite, through the retrieval and orchestration layer, to the UI a user clicks. You should be the kind of person who has gone deep on LLMs, has strong opinions about evals, and rolls their eyes at demos that don't survive contact with real users. If your strengths are general full stack engineering rather than LLM-specific work, see also our Full Stack Engineer role — it may be a closer fit. If "AI feature" still means "ChatGPT wrapper" to you, this isn't your team. If you've already shipped something into production and watched it break in interesting ways, keep reading. You'll own AI-powered features end-to-end — model choice, prompts, retrieval, tool use, the orchestration around it, the UI on top, and the evals that keep it from regressing. The work spans research-flavoured experimentation and hard product engineering, often in the same week. Concretely, in your first six months you'd expect to: - Ship at least one significant AI feature into production — agent, RAG flow, generative UI, or similar - Build out our eval and observability stack so we can ship LLM changes with confidence rather than vibes - Drive a measurable improvement on a key model-quality metric (accuracy, latency, cost, hallucination rate) - Help shape our internal AI engineering practices — model selection, prompt versioning, regression testing Day-to-day, you'll write code (a lot of it AI-assisted), design and evaluate prompts, debug agent failures, partner with product and design on what AI features should even be, and make calls on trade-offs between cost, latency, and quality. Qualifications - A genuine willingness to learn. - Adaptability across stacks. - Strong fundamentals. - System design at both altitudes. - Sharp problem-solving. - Hands-on experience building AI features in production. - Evals as a first-class skill. - Production realism about LLM features. - Strong full stack engineering chops. - Comfortable with both database paradigms. - Production cloud experience. - Fluency with AI-assisted development. - High autonomy. Requirements - Real features that real users hit, with real consequences when they fail. - Experience with several of: - LLM APIs (Anthropic, OpenAI, or open-weight models via vLLM/Bedrock/Together) and have opinions on which to use when - Retrieval-augmented generation: chunking strategies, embeddings, vector stores (pgvector, Pinecone, Weaviate, Qdrant), hybrid search - Agentic systems: tool use, multi-step workflows, planning, MCP, frameworks like LangGraph/Inngest/Temporal or your own orchestration - Structured output, function calling, and JSON-mode reliability - Streaming responses and the UX patterns that make them feel good - Prompt engineering as a serious discipline — versioning, A/B testing, regression suites, not vibes - Production realism about LLM features. - Strong full stack engineering chops. - Comfortable with both database paradigms. - Production cloud experience. - Fluency with AI-assisted development. - High autonomy. Nice to have - Fine-tuning or post-training experience (LoRA, RLHF, DPO) — even small-scale - Built or contributed to open-source AI infra, agent frameworks, or eval tooling - Experience with model routing, prompt caching, or other cost/latency optimizations at scale - Worked on AI safety, red-teaming, prompt injection defence, or content moderation pipelines - Background in IR, NLP, or applied ML before the LLM era - Strong writing — this team values it, and AI features live or die by precise language - Prior experience in a small, fast-moving team (under ~30 engineers) Got questions? You can email us at talentsupport@xplortechnologies.com

• Design, build, and implement an end-to-end AI-powered tender response platform. • Develop and maintain backend services and AI workflows using AWS-native technologies. • Build and manage infrastructure using Terraform and cloud-native best practices. • Implement AI and retrieval workflows leveraging AWS Bedrock and vector database technologies such as PGvector. • Design and orchestrate scalable workflows using AWS Step Functions. • Collaborate closely with senior technical stakeholders to deliver project milestones within tight timelines. • Contribute to architecture decisions, backend integrations, and deployment workflows. • Ensure solutions are scalable, maintainable, and production-ready. • Troubleshoot and resolve technical issues across infrastructure, backend services, and AI components. • Support testing, deployment, and optimisation activities throughout the project lifecycle. • Operate effectively in a fast-paced environment with high ownership and minimal oversight.

• Diseñar y construir sistemas de agentes de IA capaces de ejecutar flujos de trabajo autónomos (agentic workflows), orquestando múltiples modelos, herramientas y fuentes de datos para resolver problemas complejos de negocio. • Desarrollar con programación asistida por IA (Claude Code, Cursor, Copilot…), maximizando tu productividad y la del equipo. • Implementar soluciones de IA Generativa en producción : sistemas RAG avanzados, chatbots inteligentes, pipelines de procesamiento de lenguaje natural, generación de contenido y automatizaciones basadas en LLMs. • Integrar la IA en el stack de producto , conectando soluciones inteligentes con el frontend, backend, APIs y plataformas de datos existentes de nuestros clientes. • Experimentar y prototipar rápido : alineado con nuestra filosofía de iteración de alta frecuencia, probarás nuevos modelos, técnicas y arquitecturas para validar hipótesis de crecimiento con IA. • Contribuir a la estrategia de IA de Product Hackers , identificando oportunidades para aplicar inteligencia artificial al Growth de productos digitales y proponiendo nuevas líneas de servicio.

Our mission is to transform how people and machines work together to push the boundaries of human productivity. A leader in Industrial AI, Augury helps the world’s manufacturers leverage real-time production insights to drive new levels of efficiency. Combining predictive and prescriptive AI technology with industry expertise, production teams can proactively address alerts, minimize downtime, reduce asset costs, and maximize yield and capacity. Our customers achieve payback in six months or less, enabling global scale. We're looking for team members excited to partner with the world's manufacturers and build the future of production together. About the RoleWe are looking for a Senior AI Software Engineer to join the GenAI Infrastructure team and build production-grade GenAI capabilities and the infrastructure behind them. This is a hands-on engineering role focused on AI agents, RAG pipelines, tool-calling workflows, backend services, data-access layers, evaluation, observability, and reusable GenAI infrastructure used across product teams. The role is not focused on model research or prompt design alone. We are looking for a strong software engineer who can design, build, ship, and operate reliable AI-powered systems in production. What You’ll Do - Build reusable infrastructure, SDKs, and internal frameworks for AI-powered product capabilities. - Design and implement AI agents, RAG flows, tool-calling workflows, and LLM orchestration pipelines. - Build production-grade GenAI services, APIs, and backend infrastructure. - Integrate LLM workflows with internal microservices, data platforms, vector search, and event-driven systems. - Design secure data-access patterns that enforce authorization, tenant separation, and user-level scope. - Implement evaluation, tracing, monitoring, and quality-control mechanisms for GenAI systems. - Improve latency, reliability, fallback behavior, cost efficiency, and production readiness. - Work on customer-facing AI experiences, including conversational and proactive agentic product flows. - Collaborate with backend engineers, product managers, data engineers, AI/ML engineers, and domain experts. Basic Qualifications - 6+ years of professional software engineering experience. - Strong Python development skills. - Strong backend engineering background, including APIs, services, integrations, or microservices. - Proven experience designing, shipping, or operating LLM-powered applications or GenAI systems. - Experience with RAG, AI agents, tool/function calling, prompt orchestration, evaluation, and observability. - Experience with microservices, distributed systems, and production backend architecture. - Strong understanding of system design, reliability, security, scalability, latency, and maintainability. - Ability to work with complex data models and expose them safely through AI systems. - Ability to operate in ambiguous technical areas and turn prototypes into production-ready systems. - Strong communication skills and ability to explain technical decisions clearly. Preferred Qualifications - Experience with LangChain, LangGraph, or LangSmith. - Experience with Go, MongoDB, Databricks, Kubernetes, Docker, REST APIs, vector search, or event-driven systems. - Experience with Azure OpenAI, Gemini, or similar LLM platforms. - Experience building or using knowledge graphs, GraphRAG, or graph databases such as Neo4j. - Experience building multi-agent systems or orchestrating multiple specialized agents/tools in production. - Experience building internal platforms, SDKs, developer tools, or shared engineering infrastructure. - Experience with authorization, data segregation, multi-tenant systems, or user-level data scoping. - Experience in industrial, IoT, predictive maintenance, manufacturing, or operational-data domains. What Success Looks Like - You build reusable infrastructure that accelerates GenAI development across teams. - You ship reliable GenAI capabilities that run in production. - You improve how AI agents retrieve data, call tools, enforce permissions, and generate grounded responses. - You contribute to practical standards around GenAI architecture, evaluation, monitoring, security, and deployment. Perks - Stock options - Paid parental leave - Flex PTO Augury is a people-first organization. We believe in fostering an inclusive environment in which employees feel encouraged to share their unique perspectives, leverage their strengths, and act authentically. We know that diverse teams are strong teams, and we welcome those from all backgrounds and varying experiences. We are committed to providing employees with a work environment free of discrimination and harassment. We believe that diversity is more than just good intentions, and we are committed to creating an inclusive environment for all employees. Augury is a proud equal opportunity employer, we strive to create a work environment in which everyone, all applicants, employees, customers, guests, and vendors feel safe and comfortable. We commit to maintain a workplace that is free of any type of harassment and does not tolerate anyone intimidating, humiliating, or hurting others. We prohibit willful discrimination based on age, gender, ethnicity, race, color, religion, political opinions, sexual orientation, sexual identity or expression, military or veteran status, disability or any other characteristic protected by law.