ML Engineer

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ML Engineer

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

Experience: 5+ Years

Location : Hyderabad

About The Role

We are seeking a highly skilled and experienced Machine Learning Engineer to lead the design and implementation of cutting-edge ML solutions across our organization. You will be responsible for prototyping and taking models all the way from proof-of-concept to production deployment. This role requires a deep understanding of ML algorithms, data processing pipelines, model optimization, and production-grade engineering practices.

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

Contributions to open-source ML projects or research publications.

Experience with deep learning, NLP, computer vision, or reinforcement learning.

Familiarity with data privacy, AI ethics, and governance frameworks.

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