Engineering Manager

Who We Are & Why Join Us

Avathon is revolutionizing industrial AI with a powerful platform that enables businesses to harness the full potential of their operational data. Our technology seamlessly integrates and contextualizes siloed datasets, providing a 360‑degree operational view that enhances decision‑making and efficiency. With advanced capabilities like digital twins, natural language processing, normal behavior modeling, and machine vision, we create real‑time virtual replicas of physical assets, enabling predictive maintenance, performance simulation, and operational optimization.

Our AI‑driven models empower companies with scalable solutions for anomaly detection, performance forecasting, and asset lifetime extension—all tailored to the complexities of industrial environments.

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.

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As an Engineering Manager at Avathon, you will lead the design and delivery of scalable data infrastructure and AI platforms powering industrial intelligence solutions. 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 5‑7 years of experience including team leadership, you will build and grow a high‑performing engineering team across onshore and offshore locations.

• 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, Dev Ops best practices, and security compliance standards
• Scale engineering processes for multi‑customer deployments in regulated and government environments

• 5‑7 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 system reliability, fault tolerance, and high‑availability architectures
• Experience managing projects with customer‑facing delivery components
• Excellent cross‑functional communication skills

• Experience in industrial AI, manufacturing, energy, utilities, or government sectors
• Exposure to edge computing architectures and data ingestion from industrial systems (OPC UA, Modbus, Ignition, PI Historian)
• Experience working with secure or regulated cloud environments, including on‑prem and air‑gapped deployments
• Familiarity with data governance and compliance frameworks
• Understanding of predictive…