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Protocol Intelligence
Data-driven signals on your job's competitivenessMid-level role, popular ML title, metro location, and broad skill requirements create high competition.
Core ML engineering skills are transferable, but domain specializations (NLP/CV) increase sensitivity to medium.
Explicit 4–6 years plus mandatory ML, deployment, and cloud skills make shortlisting strict (high).
Job Description
Structured overview of role & requirementsAbout This Role
Design, implement, and deploy machine learning models with robust pre-processing and post-processing logics to ensure data quality, model accuracy, and business usability.
Collaborate with data scientists and data engineers to transition ML prototypes into scalable, production-ready systems ensuring data integrity and availability.
Deploy, monitor, and maintain ML models in cloud and containerized environments, managing model performance, drift, and MLOps practices including versioning and continuous integration.
Minimum Requirements
4–6 years of hands-on experience as an ML Engineer, Data Scientist, or Applied AI Engineer.
Bachelor’s or Master’s degree in Computer Science, Data Science, AI/ML, or a related field.
Proficiency in Python with libraries such as Pandas, NumPy, Scikit-learn, TensorFlow, or PyTorch.
Experience deploying ML models in cloud (AWS, GCP, Azure) and containerized environments (Docker, Kubernetes).
Ideal Candidate Profile
Experienced in designing custom pre-processing and post-processing logic for ML workflows to improve prediction quality and business alignment.
Skilled in collaborating across data science and engineering teams to build scalable and maintainable ML systems.
Comfortable working with cloud-based ML platforms and MLOps best practices including model versioning, reproducibility, and continuous integration.