Machine Learning Engineer

Augury Augury · Vertical AI · Bengaluru India · R&D

Machine Learning Engineer role focused on building Agentic AI systems for industrial applications, involving end-to-end ML lifecycle management, time-series modeling, and generative AI techniques.

What you'd actually do

  1. Own the full machine learning lifecycle: problem scoping, data exploration, pipeline development, model training, deployment, and monitoring in production.
  2. Design and build Agentic AI systems that combine time-series modeling, signal processing, and generative AI (LLMs, embeddings, orchestration frameworks, and tool use).
  3. Develop and deploy machine learning models for forecasting, anomaly detection, and pattern recognition in industrial sensor data.
  4. Integrate classical statistical methods, deep learning, and GenAI techniques to generate actionable insights from complex datasets.
  5. Build scalable data pipelines and ML systems that operate reliably in production environments.

Skills

Required

  • 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).
  • Bachelor’s degree in Computer Science, Engineering, or related technical field (B.Tech / B.E. or equivalent).

Nice to have

  • Master’s degree (M.Tech or equivalent) is a plus but not required.
  • 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.

What the JD emphasized

  • Agentic AI systems
  • Industrial AI
  • end-to-end ML systems in production environments
  • time-series modeling
  • generative AI systems
  • Agentic applications or LLM-powered systems
  • production ML systems

Other signals

  • Agentic AI systems
  • Industrial AI
  • End-to-end ML lifecycle
  • Generative AI
  • Time-series modeling