Senior Technical Lead

Experience: 5 to 10 years

Location: Bangalore, India

Skills: Timeseries ML, constrained optimization, facility guardrails, pressure system feature engineering, DHP, WHP, Manifold, OT/historian (PI) data wrangling, Azure ML, CI/CD, HPC-trained models, Docker, ONNX model packaging, FastAPI, Flask, Python, NumPy, Pandas, PyTorch, Scikit-learn, PySpark, Delta Lake, ADF, Azure Databricks, Azure DevOps, YAML pipelines, Application Insights, Azure Monitor, data drift monitors, Git, testing frameworks, logging & monitoring best practices, artificial lift systems, ESP, gas lift, hydraulic flow models, physics-based modeling, surrogate modeling, hybrid ML+physics workflows, realtime streaming, Event Hubs, Kafka, IoT Hub

Job Summary
Years of Experience: 5-10 years, Work timing - 01:30 -10:30 PM Preferable: Production Engineering knowledge Oil & Gas industry Timeseries ML and constrained optimization for production ramp/sequence planning; ability to encode facility guardrails and drawdown targets. Pressure system feature engineering using DHP/WHP/Manifold/Up/Downstream signals; comfort reconciling telemetry with physical intuition. OT/historian (PI) data wrangling at scale; robust handling of gaps, sensor drift, and event slicing for ramp windows. Azure ML model packaging, endpoints, monitoring; hands-on with CI/CD for retrains and can migrate from HPC-trained models to cloud-served artifacts. Operator-centric delivery: translating model outputs into clear ramp steps/visual cues (stoplights, countdowns) and validating against control room practice.

Key Responsibilities
A highly skilled Machine Learning Engineer with 5–10 years of experience in timeseries forecasting, sensor-level feature engineering, and optimization models for industrial/energy systems.



Strong background working with PI historian, operational telemetry, and production facility constraints. Proficient in designing end-to-end ML pipelines—from OT data extraction and feature engineering to model deployment, monitoring, and operator-centric UI delivery. Adept at translating complex ML/optimization outputs into explicit operational instructions used by field/production teams.

• Senior ML Engineer (6+ years)
• Applied Scientist – Energy Optimization
• OT Data + ML Specialist
• Azure ML: Pipelines, endpoints, environments, registries
• ADF and Azure Databricks (PySpark, Delta Lake, DLT)
• CI/CD via Azure DevOps — YAML pipelines, automated retrains
• Monitoring: Application Insights, Azure Monitor, data drift monitors
• Containerization (Docker), ONNX model packaging
• Working knowledge of containerization (Docker) and API deployment (FastAPI/Flask).

Skill Requirements
Experience in oil & gas, energy, or industrial automation environments. Exposure to artificial lift systems (ESP/gas lift) or hydraulic flow models. Knowledge of physics-based modeling, surrogate modeling, or hybrid ML+physics workflows. Experience with realtime streaming (Event Hubs, Kafka, IoT Hub).

Shape
• Software Engineering Skills
• Python (NumPy, Pandas, PyTorch, Scikit-learn)
• PySpark, Delta Lake, ADLS
• REST APIs (FastAPI/Flask)
• Git, testing frameworks, logging & monitoring best practices

Shape
• Soft Skills
• Strong cross-functional communication with production engineers, operators, SMEs
• High ownership & ability to simplify complex ML outputs
• Can work with ambiguity and evolving requirements
• Comfortable leading design discussions and technical decisions

Other Requirements
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📌 Senior Technical Lead (Bengaluru)
🏢 HCL Technologies
📍 Bengaluru