ML Engineer

• by HR Neev Systems
• October 21, 2025October 21, 2025
  

About The Role

Machine Learning Engineer

Key Responsibilities

• Build and validate ML prototypes to solve real business problems
• Develop, test, and optimize ML models using structured and unstructured data
• Design and implement scalable data pipelines and model serving infrastructure
• Continuously monitor, improve, and re-train models in production
• Ensure reproducibility, versioning, and documentation of models and experiments
• Evaluate and select appropriate ML tools, frameworks, and technologies to meet business requirements.
• Oversee the full ML lifecycle including data preparation, model development, training, validation, deployment, and monitoring.
• Collaborate with stakeholders to translate business needs into ML solutions.
  

Required Skills & Qualifications

• Bachelor’s or Master’s degree in Computer Science, Data Science, AI/ML, or related field.
• 5+ years of professional experience with at least 3 years in machine learning and data science.
• Take at least one ML solution from idea to full-scale production deployment
• Strong programming skills in Python, with experience in ML libraries like TensorFlow, PyTorch, Scikit-learn, XGBoost, etc.
• Deep understanding of ML architecture patterns, data pipelines, and distributed systems.
• Experience with any of cloud platforms like AWS, Azure, or GCP and cloud-native ML tools (SageMaker, Vertex AI, Azure ML).
• Proficiency in Docker, Kubernetes, and other containerization/orchestration tools.
• Strong grasp of MLOps, model monitoring, and continuous integration/deployment pipelines.
• Hands-on experience with big data technologies like Spark, Hadoop, Hive, or similar.
  

Preferred Qualifications

deep learning

NLP

computer vision

reinforcement learning

data privacy

AI ethics

governance