Augury

Industrial AI

MLOpsEngineer

₹25–40L ~AI est. Bengaluru, India
Market Sentiment
HIGH DEMAND

Neural analysis suggests this role is
optimal for Senior candidates.

The Brief

“ML Ops Engineer at Augury. Skills: MLOps, AI systems, Production platform, ML lifecycle. Design production MLOps capabilities. Build systems for experiment tracking”

Industry & Context.

Industrial AI
Problems you'll solve

Root cause analysis

What They're Looking For.

Must Have

5+ years software engineering, 5+ years MLOps, 5+ years ML platform engineering, Python engineering skills, End-to-end ML lifecycle understanding, Experience with large-scale data platforms, Experience with ML platform frameworks, Proven ability to design workflow orchestration, Written and verbal communication skills

Nice to Have

Experience with industrial platforms, Experience with IoT platforms, Experience with manufacturing platforms, Experience with feature stores, Experience with model registries, Experience with dataset versioning, Experience with lineage systems, Experience with AI agents, Experience with RAG systems, Experience with production GenAI applications, Experience with evaluation frameworks

What You'll Do.

Design production MLOps capabilities

Build systems for experiment tracking

Build systems for artifact management

Build systems for reproducibility

Build systems for versioning

Build systems for lineage

Build systems for promotion workflows

Build systems for production readiness

Develop reusable platform tooling

Develop engineering standards

Build operational infrastructure for LLM

Build operational infrastructure for agentic systems

Design evaluation frameworks for AI systems

Design monitoring frameworks for AI systems

Build large-scale training pipelines

Write Python services

Write platform libraries

Drive engineering quality

Implement automated testing

Implement observability

Implement deployment standards

Implement operational best practices

How You'll Work.

Team & Collaboration

Across teams

Communication Scope

Written communication; Verbal communication

Full Job Description

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. We are looking for a MLOps Engineer with strong production engineering experience building and operating scalable ML and AI systems. This is a software-first MLOps platform role focused on production reliability, ML lifecycle management, large-scale training infrastructure, operational AI systems, and reusable platform capabilities. You will help build and scale the production platform behind Augury’s Industrial AI Workforce, enabling teams across the company to develop, evaluate, deploy, and operate ML and AI systems consistently and safely. A Day In Your Life Design and evolve production MLOps capabilities across the full ML lifecycle including datasets, features, models, evaluations, deployments, monitoring, retraining, and feedback signals. Build systems for experiment tracking, artifact management, reproducibility, versioning, lineage, promotion workflows, and production readiness. Develop reusable platform tooling, golden paths, and engineering standards that improve consistency and delivery velocity across teams. Build operational infrastructure for LLM and agentic systems including prompts, tools, traces, evaluations, observability, safety boundaries, and production monitoring. Design evaluation and monitoring frameworks for AI systems including answer quality, latency, grounding, reliability, and operational regressio

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