Machine Learning Engineer

Job Description

Job Description

Machine Learning EngineerJob Summary

We are looking for a talented Machine Learning Engineer to design, develop, and deploy scalable machine learning models that solve complex business problems. The ideal candidate should have experience in data preprocessing, model development, feature engineering, and deploying ML solutions in production environments. You will work closely with data scientists, software engineers, and product teams to build intelligent applications.

• Design, build, and deploy machine learning models for predictive analytics and automation.
• Collect, clean, and preprocess structured and unstructured datasets.
• Perform feature engineering and model optimization to improve performance.
• Train, validate, and evaluate machine learning models using industry best practices.
• Deploy ML models using cloud platforms and containerization technologies.
• Monitor model performance and retrain models as needed.
• Collaborate with cross-functional teams to understand business requirements.
• Develop APIs and services for model inference.
• Document model architecture, experiments, and deployment processes.
• Stay updated with the latest advancements in AI and machine learning technologies.

• Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Data Science, or a related field.
• 3–6 years of experience in Machine Learning or Artificial Intelligence.
• Strong programming skills in Python.
• Experience with supervised and unsupervised learning algorithms.
• Hands-on experience with TensorFlow, PyTorch, or Scikit-learn.
• Knowledge of statistics, probability, and linear algebra.
• Experience with SQL and NoSQL databases.
• Familiarity with REST APIs and microservices architecture.
• Experience with cloud platforms such as AWS, Azure, or Google Cloud Platform.
• Understanding of CI/CD pipelines for ML deployment.

• Python
• Machine Learning
• Deep Learning
• TensorFlow
• PyTorch
• Scikit-learn
• Pandas
• NumPy
• Feature Engineering
• Model Deployment
• Data Preprocessing
• SQL
• Docker
• Kubernetes
• Git

• NLP (Natural Language Processing)
• Computer Vision
• MLOps
• Apache Spark
• MLflow
• Airflow
• Kafka
• Azure ML
• AWS SageMaker
• Google Vertex AI
• FastAPI
• Flask

• Experience with large-scale ML model deployment.
• Knowledge of Generative AI and Large Language Models (LLMs).
• Experience with vector databases such as Pinecone, Milvus, or FAISS.
• Familiarity with prompt engineering and Retrieval-Augmented Generation (RAG).
• Experience with Agile/Scrum methodologies.

3–6 Years

Full-Time

Remote / Hybrid / On-site

$110,000 – $145,000 per year (Based on experience and location)