Machine Learning Engineer

Augury is building AgenticAI systems that transform how industrial organizations understand and optimize their operations. As a Senior ML Engineer on this team, you will help turn that vision into reality.

You will be part of the AgenticAI team, you will work closely with central Algorithm and Product teams to design, build, and deploy intelligent systems that power next-generation industrial AI applications.

You will own the end-to-end lifecycle of machine learning solutions—from problem definition and data exploration to model development, deployment, and production monitoring. Your work will span classical machine learning, time-series modeling, and generative AI to build agent-based systems that turn complex industrial data into actionable intelligence.

You will architect and implement Agentic applications that leverage heterogeneous data sources, including sensor-based time-series signals, unstructured text, and existing ML frameworks. These systems will directly power customer-facing workflows, delivering measurable operational impact for industrial manufacturers.

This role sits at the intersection of Agentic AI and Industrial AI, where your work will directly shape how customers interact with Augury’s platform and realize value from their data.

• Own the full machine learning lifecycle: problem scoping, data exploration, pipeline development, model training, deployment, and monitoring in production.
• Design and build Agentic AI systems that combine time-series modeling, signal processing, and generative AI (LLMs, embeddings, orchestration frameworks, and tool use).
• Develop and deploy machine learning models for forecasting, anomaly detection, and pattern recognition in industrial sensor data.
• Integrate classical statistical methods, deep learning, and GenAI techniques to generate actionable insights from complex datasets.
• Build scalable data pipelines and ML systems that operate reliably in production environments.
• Collaborate with Product, Engineering, and Algorithm teams to define solutions and translate customer needs into technical implementations.
• Partner with customers and internal stakeholders to identify new data-driven opportunities and emerging use cases.
• Implement robust evaluation, monitoring, and drift detection systems for live ML/Agentic applications.

• Bachelor’s degree in Computer Science, Engineering, or related technical field (B.Tech / B.E. or equivalent).
• Master’s degree (M.Tech or equivalent) is a plus but not required.
• Equivalent industry experience will be considered for exceptional candidates.
• 4+ years of experience in Machine Learning, Applied AI, or related fields.
• Proven ability to own and deliver end-to-end ML systems in production environments.
• Strong experience in time-series modeling, forecasting, anomaly detection, and feature engineering.
• Hands-on experience with Python and ML frameworks (e.g., Pydantic, PTorch, TensorFlow, Scikit-learn, or similar).
• Familiarity with generative AI systems, including LLMs, embeddings, and agent-based architectures (e.g. Langchain, LangGraph, DSPy)
• Experience building or working with Agentic applications or LLM-powered systems.
• Strong understanding of data pipelines and production ML systems (deployment, monitoring, retraining, drift detection).
• Experience working in Agile, fast-paced, cross-functional environments.
• Strong collaboration skills with distributed teams across engineering, product, and data functions.

• Experience in industrial AI, IoT, predictive maintenance, or manufacturing systems.
• Exposure to digital twins, context graphs, or knowledge graph-based systems.
• Familiarity with modern data platforms such as Databricks, BigQuery, Snowflake, or similar.
• Understanding of optimization frameworks (e.g., Pyomo, Gurobi, OR-Tools).
• Experience working with large-scale distributed data systems.