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Engineering Manager (SCM Planning) - Product Engineering

Avathon
CompanyAvathon
CategoryUncategorised
LocationBengaluru
RemoteOn-site (inferred)
EmploymentNot stated
LevelNot stated
SalaryNot stated by the employer
Posted12 Jul 2026
Last verified2 Aug 2026
SourceEmployer ATS (greenhouse)
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Description
Who We Are & Why Join Us Avathon is the leading Industrial AI autonomy platform, helping customers across heavy industries -- energy, mining, manufacturing, aerospace, defense, and logistics -- accelerate the journey toward autonomous operations. Our platform is built on a Computational Knowledge Graph foundation that contextualizes and connects operational data across siloed systems, bringing together time series, structured, unstructured, and machine vision data to power AI-driven applications in asset performance management, supply chain intelligence, visual AI, and global trade management. With capabilities spanning digital twins, normal behavior modeling, natural language processing, and computer vision, Avathon delivers real-time predictive intelligence and agentic decision-making at industrial scale. Cutting-Edge AI Innovation -- Join a team at the forefront of AI, developing groundbreaking solutions that shape the future. High-Growth Environment -- Thrive in a fast-scaling startup where agility, collaboration, and rapid professional growth are the norm. Meaningful Impact -- Work on AI-driven projects that drive real change across industries and improve lives. Learn more at:  avathon.com   Engineering Manager – SCM Planning Work Location: Bangalore Five Days onsite About the Role As an Engineering Manager – SCM Planning at Avathon, you will lead the design and delivery of scalable data infrastructure and AI platforms powering industrial intelligence solutions in the areas of demand forecasting, supply planning, inventory & Manufacturing and S&OP decision support for customers across energy, mining, manufacturing, and logistics. You will drive cloud-native architectures, production-grade data pipelines, and AI-ready systems that enable advanced analytics and Generative AI applications. Partnering with Engineering, Product, and Applied Science teams, you will translate complex industrial challenges into secure, scalable technology solutions while shaping Avathon's platform engineering strategy. With 8+ years of experience including team leadership, you will build and grow a high-performing engineering team across onshore and offshore locations. You Will Build and lead an engineering team responsible for Avathon's data and AI platform infrastructure Architect scalable industrial data platforms capable of handling high-velocity IoT and telemetry streams Design resilient real-time and batch data pipelines that power predictive maintenance, anomaly detection, and optimization use cases Establish MLOps frameworks to support continuous model training, deployment, and monitoring Partner with AI/ML, Product, and Service Engineering teams to deliver production-ready solutions to customers Ensure seamless production handoffs with strong documentation, observability, and SLAs Drive cloud infrastructure, DevOps best practices, and security compliance standards Scale engineering processes for multi-customer deployments in regulated and government environments You'll Have 8+ years of experience in software engineering, data engineering, or AI platform engineering 2+ years leading technical teams (onshore + offshore preferred) Experience designing cloud-native data architectures across AWS, GCP, or Azure Strong experience with: Distributed data processing (Spark, Databricks) Streaming technologies (Kafka, Kinesis, IoT Core) Workflow orchestration (Airflow, Dagster, Prefect) Containerization and orchestration (Docker, Kubernetes) Experience building and operating production MLOps systems including CI/CD for ML, model registry, experiment tracking, and drift detection Strong programming expertise in Python and Django Familiarity with React frontend development Experience with CI/CD pipelines (Travis CI, Jenkins) Experience working with large-scale time-series or industrial telemetry data Solid understanding of s
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