Advanced engineer – embedded machine learning Jobs – 141 Open Jobs Today

Data Scientist (Contract6 month)
Employe Hub —Delhi, Delhi
- ₹6,00,436.05 - ₹19,75,141.93 a year
- Part-time
- Azure
- Salesforce
- Engineering
Quick apply
21h
Seniorstaff-C++ , Machine Learning
Qualcomm —Hyderabad, Telangana
- Doctoral degree
- Computer Science
- Engineering
18h
Senior Engineer
Qualcomm —Bengaluru, Karnataka
- Doctoral degree
- Computer Science
- Engineering
18h
Data Scientist
Shell Energy —Chennai, Tamil Nadu
- Full-time
- Azure
- Computer Science
- Salesforce
13d
Asset & Wealth Management - PWM Client Communications - Associate - Hyderabad
Goldman Sachs —Hyderabad, Telangana
- Computer Science
- B.Sc
- Java
- Health insurance
1d
Senior Manager – Data Scientist
Genpact India Pvt. Ltd. —Gurugram, Haryana
- Full-time
- Azure
- Computer Science
- English
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6d
Data Science Engineer
beON Consult GmbH („beON“) —India
- Full-time
- Remote
- Azure
- Computer Science
- English
Salesforce Technical Lead
Algoworks Solutions —India
- Full-time
- Remote
- Salesforce
- Microsoft Office
- Master's degree
AI Soution Architect
Algoworks Solutions —India
- Full-time
- Remote
- Azure
- Computer Science
- System design
Robotics Engineer
XLNT Technology —Ravet, Pune, Maharashtra
- ₹20,000 - ₹40,000 a month
- Full-time
- Master's degree
- C++
- Electrical experience
- Health insurance
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18h
Lead Engineer, Senior
Qualcomm —Bengaluru, Karnataka
- Doctoral degree
- Computer Science
- Engineering
1d
Lead Engineer, Senior
Qualcomm —Bengaluru, Karnataka
- Doctoral degree
- Computer Science
- Engineering
1d
R4 Data Engineering
Eli Lilly —Hyderabad, Telangana
- Full-time
- Computer Science
- System design
- Master's degree
13h
Chief Data Scientist 2
Genpact India Pvt. Ltd. —Bengaluru, Karnataka
- Full-time
- Hybrid work
- Azure
- Computer Science
- English
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14h
Sr. Data Scientist 4B
Genpact India Pvt. Ltd. —Bengaluru, Karnataka
- Full-time
- Hybrid work
- Computer Science
- English
- Master's degree
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16h
Principal Consultant – Tech Project Manager (GenAI Project Manager)
Genpact India Pvt. Ltd. —Hyderabad, Telangana
- Full-time
- Hybrid work
- Computer Science
- English
- Master's degree
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14h

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Seniorstaff-C++ , Machine Learning
Qualcomm —Hyderabad, Telangana
As a Qualcomm Software Engineer, you will design, develop, create, modify, and validate embedded and cloud edge software, applications, and/or specialized…
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Data Scientist (Contract6 month)

Employe Hub
Delhi, Delhi

Quick apply

Job details

Part-time | Full-time
₹6,00,436.05 - ₹19,75,141.93 a year
21 hours ago

Qualifications

Azure
Internet of things
SAP
Salesforce
Engineering
NumPy
Git
Finance
Master's degree
SQL
Pandas
OOP
AWS
Bachelor's degree
Machine learning
Quality control
SciPy
Distributed control systems
Data science
Manufacturing
SAP ERP
Python

Full job description

ou will build and deploy machine-learning models directly on plant data to cut energy consumption, improve equipment reliability, and tighten product quality across our cement operations. This is a hands-on modelling role embedded with process, operations and reliability teams — your work will be measured in real, finance-validated savings (kcal/kg clinker, kWh/tonne, avoided downtime), not slide decks.

Key responsibilities

Build, validate and deploy ML models for process optimisation (kiln / pyro-process control, grinding & separator efficiency), predictive maintenance on critical rotating equipment, and quality / clinker-factor optimisation.

Work with high-frequency sensor and time-series data from plant historians, DCS and IIoT systems; engineer meaningful features from noisy, real-world industrial signals.

Partner with plant operators and process engineers to encode domain knowledge into models, and to take models safely from advisory recommendations toward closed-loop control.

Establish rigorous baselines and quantify impact with finance-grade discipline; defend results under scrutiny.

Work with the MLOps / platform team to productionise models and monitor them in live operation.

Communicate findings clearly to non-technical plant leadership.

Required qualifications (must-have):

Bachelor's or Master's in Engineering (Chemical, Mechanical, Electrical, Industrial), Statistics, Computer Science, or a related quantitative field.

3–6 years building and deploying ML models, including demonstrable experience in a manufacturing or process-industry environment (cement, steel, refining, chemicals, power, glass, mining, or similar).

Strong applied skills in time-series analysis, sensor/signal data, anomaly detection, regression and forecasting, with a solid statistics foundation.

Strong, idiomatic Python for data science (NumPy, pandas, SciPy, scikit-learn, statsmodels) with clean, tested, production-quality code; strong SQL.

Deep command of classical / traditional machine learning — regularised regression (Ridge, Lasso, ElasticNet), tree-based ensembles (Random Forest, Gradient Boosting — XGBoost / LightGBM / CatBoost), SVM, k-NN and Naive Bayes — with sound feature engineering, cross-validation and hyperparameter tuning.

Proven ability to wrangle messy industrial data and engineer features that work in production.

Comfortable on the plant floor — explaining models to engineers and operators and earning their trust.

Preferred (strong pluses):

· Hands-on experience with Industrial IoT (IIoT) and Operational Technology (OT) data — plant historians (OSIsoft PI / AVEVA, Aspen IP.21), OPC-UA, SCADA / DCS, time-series databases.

· Domain exposure to cement or heavy/process manufacturing (pyroprocessing, grinding, combustion, quality control).

Experience working with data from SAP (ERP — especially PM / PP / production & maintenance modules) and Salesforce (SFDC).

Familiarity with Advanced Process Control (APC) concepts and closed-loop deployment.

Deep learning for time series; physics-informed or hybrid (data + first-principles) modelling.

Technical skills:

Programming & engineering: idiomatic, production-quality Python — NumPy, pandas and SciPy for vectorised data work; clean, modular code with unit tests (pytest); OOP; virtual environments & packaging; Jupyter; Git. Strong SQL; PySpark for large datasets a plus.

Classical machine learning: hands-on depth across regularised regression, tree-based ensembles (Random Forest, XGBoost / LightGBM / CatBoost), SVM, k-NN and Naive Bayes; unsupervised methods — k-means, DBSCAN, hierarchical clustering and PCA / dimensionality reduction.

Statistical & modelling rigour: hypothesis testing, regression diagnostics, feature engineering & selection, cross-validation, hyperparameter tuning, class-imbalance handling, and disciplined error analysis.

Time-series & anomaly detection: classical methods (ARIMA / SARIMA, exponential smoothing, state-space models) and libraries (statsmodels, sktime, tsfresh, Prophet); anomaly detection (Isolation Forest, One-Class SVM).

Core libraries: scikit-learn, statsmodels, XGBoost / LightGBM, matplotlib / seaborn.

Platform & tooling

Cloud / lakehouse (Azure, AWS or Databricks); plant historian & OT connectors; Git-based workflows.

Pay: ₹600,436.05 - ₹1,975,141.93 per year

Work Location: In person

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