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Machine Learning Engineer

Machine Learning Engineer

Siemens

2 - 7 years

0 Lacs

gurugram haryana india

Posted:5 days ago| Platform: Foundit logo

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Skills Required

airflow scikit-learn mlflow dvc gcp vertex ai aws sagemaker kubeflow bentoml

Work Mode

On-site

Job Type

Full Time

Job Description

Position Summary:

The Machine Learning Engineer is responsible for designing, building, deploying, and optimizing machine learning models and data-driven solutions that support business objectives. This role bridges software engineering and data science, ensuring ML models are scalable, efficient, production-ready, and integrated seamlessly into applications and systems. The ML Engineer collaborates with data scientists, software developers, product managers, and domain experts to turn analytical prototypes into robust, high-performing ML pipelines.

A Snapshot of your Day

How You'll Make An Impact (responsibilities Of Role)

Model Development & Optimization

Develop, implement, and optimize machine learning models for classification, regression, forecasting, and other analytics tasks.
Collaborate with data scientists to refine features, algorithms, and model architecture.
Evaluate model performance using appropriate metrics and ensure continuous improvement.
ML Pipeline Engineering
Build scalable, automated ML pipelines (data preprocessing, training, validation, deployment).
Implement MLOps best practices, including CI/CD for ML, versioning, monitoring, and retraining workflows.
Use modern ML frameworks such as TensorFlow, PyTorch, Scikit-learn, or XGBoost.
Data Engineering & Preparation
Work with large, structured and unstructured datasets.
Build data processing workflows using Python, SQL, and big data tools.
Ensure data quality, consistency, and proper feature engineering.
Deployment & Productionization
Deploy machine learning models to cloud or on-premise environments (AWS, Azure, GCP).
Develop REST APIs, microservices, or batch processes to expose ML capabilities.
Monitor model performance in production and address drift, degradation, or bias issues.
Collaboration & Documentation
Work closely with software engineers to integrate ML systems into applications.
Partner with data scientists to validate model assumptions and ensure reproducibility.
Document ML workflows, architecture, and operational procedures.
Research & Innovation
Stay updated with new ML techniques, tools, and best practices.
Evaluate new technologies (e.g., LLMs, transformers, AutoML, graph ML).
Experiment with advanced models and help drive innovation within the organization.

What You Bring (required Qualification And Skill Sets)

Bachelor's or Master's degree in Computer Science, AI, Data Science, Engineering, or related field.
25+ years of experience developing and deploying machine learning solutions.
Strong experience with Python and ML libraries (TensorFlow, PyTorch, Scikit-learn, Pandas, NumPy).
Experience with cloud ML services (AWS SageMaker, Azure ML, GCP Vertex AI).
Solid understanding of mathematics and statistics related to ML (optimization, probability, linear algebra).
Knowledge of data engineering tools (Spark, Kafka, Airflow, Databricks) is a plus.
Experience building APIs and production services (FastAPI, Flask, Django).
Strong problem-solving and analytical thinking.
Curiosity about new technologies and a growth mindset.
Ability to balance experimentation with scalability and reliability.
Effective communication and teamwork skills.
Attention to detail and strong ownership of deliverables.

Preferred Qualifications

Experience with deep learning, NLP, computer vision, or time-series forecasting.
Familiarity with MLOps platforms (MLflow, Kubeflow, DVC, BentoML).
Knowledge of containerization and orchestration (Docker, Kubernetes).
Background in distributed computing or big data processing.
Experience applying ML in domain-specific environments (IoT analytics, finance, geospatial, or industrial systems).

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Machine Learning Engineer