AI/ML Engineer

Siemens

3 - 5 years

3 - 5 Lacs

Bengaluru Karnataka India

Posted:5 days ago| Platform: Foundit logo

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Skills Required

Cloud Platforms

Work Mode

On-site

Job Type

Full Time

Job Description

Develop, train, and deploy machine learning models to solve real-world problems.
Design and implement scalable AI/ML pipelines, from data preprocessing to model deployment.
Collaborate with data engineers and backend teams to integrate models into production systems.
Optimize algorithms and models for performance, accuracy, and efficiency.
Conduct exploratory data analysis to uncover insights and inform model development.
Stay up-to-date with the latest AI/ML advancements and apply them to enhance project outcomes.
Troubleshoot and refine models with minimal guidance, ensuring robustness and reliability.

Job Requirements/Skills

3-5 years of experience in AI/ML development, with a focus on practical applications.
Strong proficiency in Python and libraries such as TensorFlow, PyTorch, scikit-learn, or Keras.
Experience with data manipulation tools (e.g., Pandas, NumPy) and working with large datasets.
Knowledge of supervised and unsupervised learning techniques, NLP, or computer vision (depending on project needs).
Familiarity with cloud platforms (e.g., AWS, GCP, Azure) for model training and deployment.
Ability to work independently, prioritize tasks, and deliver high-quality results with minimal oversight.
Solid understanding of software engineering principles and version control (e.g., Git).
Excellent analytical skills and a strong foundation in statistics and mathematics.

Preferred Qualifications

Experience with MLOps tools (e.g., MLflow, Kubeflow) or containerization (e.g., Docker).
Familiarity with big data frameworks like Spark or Hadoop.
Prior work in deploying AI models in production environments or real-time systems.
Exposure to Agile methodologies or cross-functional team settings.

Key Skills

Machine Learning Model Development
Python & AI Libraries (TensorFlow, PyTorch, etc.)
Data Manipulation & Large Datasets
Supervised & Unsupervised Learning
Cloud Platforms (AWS, GCP, Azure)
MLOps & Containerization

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