Role & responsibilities

: Lead AI Engineer / AI Team Lead Heavy Industry Operations

Location: Hedri- Gadchiroli

Department: IT

Experience Required: Minimum 10 years of relevant industrial IT/OT experience, with at least 5+ years of dedicated experience developing and deploying AI/ML models in heavy industry settings.

Role Overview

We are seeking a highly experienced, domain-focused Lead AI Engineer to architect, build, and deploy data-driven machine learning models across our end-to-end mining, beneficiation, and pelletizing operations.

This is not a generic data science role. You will bridge the gap between complex operational technology (OT) data and advanced industrial AI. You will be responsible for transforming raw data from crushing circuits, flotation cells, thickeners, balling discs, and induration furnaces into real-time, closed-loop machine learning models that directly optimize ore recovery, throughput, fuel/energy efficiency, and product quality.

Key Responsibilities

Core AI/ML Architecture & MLOps

Architect and execute the deployment of Edge AI models directly onto plant servers/edge gateways to bypass network latency and ensure 24/7 standalone availability in remote areas.

Establish an MLOps pipeline explicitly designed for time-series industrial sensor drift (compensating for physical machine wear, sensor degradation, and fluctuating ambient environments).

Collaborate with automation teams to transition models smoothly from Advisory Mode (providing recommendations to operators via dashboards) to Closed-Loop Control (directly writing setpoints back to SCADA/DCS systems).

Required Skill Sets & Qualifications

Technical Competencies (AI & Data Science)

Industrial Data Literacy: Deep experience handling multi-rate time-series data, sensor anomalies, noise filtering, and data aggregation from historians (e.g., OSIsoft PI,



AspenTech InfoPlus.21).

Core Machine Learning: Proficiency in Python and open-source frameworks (TensorFlow, PyTorch, Scikit-Learn) with specialized experience in Convolutional Neural Networks (CNNs) for vision, RNN/LSTMs for time series, and classical regression/classification models.

Deployment Architecture: Direct experience deploying edge intelligence utilizing platforms like Azure IoT Edge, AWS IoT Greengrass, or containerized Docker setups embedded alongside Rockwell/Siemens/ABB plant systems.

Domain & OT Integration Skills

Clear understanding of basic mineral processing circuits, mass balancing, metallurgical recovery loops, and the physical chemistry behind iron/mineral pelletization.

Solid knowledge of automation interfaces and industrial communication protocols (Modbus, OPC UA, MQTT).

Leadership & Experience Track Record

Overall Experience: Minimum 10 years of skilled experience in technical/industrial engineering, software development, or industrial automation.

Core AI Experience: At least 5 years of hands-on experience designing, training, deploying, and maintaining production-grade AI/ML models.

Proven Delivery: Must showcase a portfolio of at least 2-3 specific AI use cases successfully deployed in a live plant environment that achieved documented business metrics (e.g., +2% recovery, -5% energy usage, or 30% reduction in out-of-spec scrap).

Change Management: Excellent communication skills to articulate model logic clearly to metallurgists, plant managers, and field operators, demystifying the AI "black box."

Preferred Qualifications

Master’s or Ph.D. in Data Science, Computer Science, Automation Engineering, Chemical/Metallurgical Engineering, or a related field with an analytical focus.

Preferred candidate profile

Prior experience working in an iron ore, copper, gold, or base metals processing facilit

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🏢 LLOYDS INFRASTRUCTURE CONSTRUCTION
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