Augury provides IIoT and AI-driven machine health solutions using sensors and edge analytics to monitor vibration, sound, temperature, deliver diagnostics, predictive maintenance, operational analytics, and parts fulfillment for manufacturers.
<div class="content-intro"><p>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.</p></div><p>As Augury’s GenAI Engineer for AgenticAI, you’ll work at the forefront of combining AgenticAI with Industrial AI to solve meaningful, real-world manufacturing challenges. You will work closely with our central Algorithm, Product, and Engineering teams to design, build, and deploy scalable GenAI and AgenticAI solutions for industrial environments. This role sits at the intersection of AI engineering, time-series analytics, distributed systems, and user-centric product development.</p> <p>You will own the end-to-end lifecycle of GenAI applications, from problem definition and experimentation to production deployment and continuous monitoring, helping bring Augury’s AgenticAI vision to life through intelligent, impactful workflows that drive measurable customer value and ROI.</p> <p><strong>A Day In Your Life</strong></p> <ul> <li>Own the end-to-end development lifecycle of GenAI and AgenticAI solutions, from experimentation and prototyping through deployment and monitoring.</li> <li>Build intelligent systems that combine time-series modeling, signal processing, and GenAI technologies including LLMs, embeddings, agents, orchestration frameworks, and retrieval pipelines.</li> <li>Design and implement LLM-powered workflows such as RAG pipelines, tool usage, multi-agent orchestration, and evaluation frameworks at scale.</li> <li>Develop AgenticAI applications that integrate diverse data sources, including sensor-based time-series data, unstructured text, and machine learning outputs.</li> <li>Drive technical decision-making across architecture, tooling, experimentation strategy, and deployment patterns for AgenticAI systems.</li> <li>Partner closely with Product, Engineering, Applied AI, and domain experts to translate customer problems into scalable technical solutions.</li> <li>Contribute to the development of data pipelines, deployment infrastructure, evaluation frameworks, and monitoring systems across MLOps and LLMOps environments.</li> <li>Build and maintain backend services in Python, including REST/gRPC APIs and workflow orchestration services connecting AI agents with platform infrastructure.</li> <li>Monitor deployed systems for performance, drift, reliability, latency, and cost efficiency, continuously improving model quality and operational scalability.</li> <li>Collaborate directly with customers and internal stakeholders to prototype and deliver innovative AI-driven experiences.</li> <li>Help shape Augury’s AgenticAI platform and user experience for industrial operators and reliability teams.</li> </ul> <p><strong>What You Bring</strong></p> <ul> <li>Bachelor’s degree in Computer Science, Information Technology, Engineering, or a related technical field (B.Tech / B.E. or equivalent).</li> <li>Master’s degree (M.Tech or equivalent) is a plus, but not required.</li> <li>Equivalent practical experience will also be considered for exceptional candidates.</li> <li>2–4 years of experience spanning Data Science, Machine Learning, AI Engineering, or GenAI development.</li> <li>Hands-on experience building and deploying GenAI or AgenticAI applications in production environments.</li> <li>Strong experience with GenAI frameworks such as LangChain, CrewAI, AutoGen, LangGraph, or similar ecosystems.</li> <li>Experience implementing LLM-based workflows including prompting, embeddings, RAG, tool calling, orchestration, and evaluation systems.</li> <li>Familiarity with evaluation methodologies such as HITL, LLM-as-a-judge, deterministic evaluation, and fine-tuning workflows.</li> <li>Experience with observability and experimentation tooling such as LangSmith or equivalent platforms.</li> </ul> <p><strong>Engineering & Systems Experience</strong></p> <ul> <li>Strong proficiency in Python for model development, backend services, deployment, and monitoring.</li> <li>Experience building scalable APIs and microservices using REST and/or gRPC.</li> <li>Understanding of modern software engineering principles, CI/CD workflows, distributed systems, and production system design.</li> <li>Experience optimizing inference performance, latency, and API usage costs across AI systems.</li> <li>Familiarity with cloud-native development patterns and scalable AI infrastructure is a plus.</li> </ul> <p><strong>Collaboration & Ways of Working</strong></p> <ul> <li>Ability to work in fast-paced Agile environments with a strong bias toward iteration, experimentation, and continuous improvement.</li> <li>Strong communication and collaboration skills with the ability to work cross-functionally across technical and non-technical teams.</li> <li>Product mindset with a focus on customer impact, usability, and business outcomes.</li> </ul> <p><strong>Nice to Have</strong></p> <ul> <li>Background in industrial systems, IoT platforms, digital twins, or manufacturing technologies.</li> <li>Familiarity with time-series modeling, anomaly detection, predictive maintenance, or sensor analytics systems.</li> <li>Experience with streaming infrastructure such as Kafka, NSQ, or similar event-driven architectures.</li> <li>Familiarity with data platforms such as Databricks, BigQuery, Snowflake, or lakehouse architectures.</li> <li>Experience working with knowledge graphs, context graphs, or data-intensive AI applications.</li> <li>Exposure to mult