AI/ML Engineer

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PrismHR

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Job Summary


Job Type
-

Seniority

Years of Experience
Information not provided

Tech Stacks
Python Hadoop Kubernetes Spark Azure PyTorch CI Analytics Docker AWS TensorFlow

Job Description

We are seeking a highly skilled AI/ML Engineer to design, develop, and deploy machine learning models and AI-driven solutions that enhance business processes and product capabilities. The ideal candidate will have strong expertise in data science, algorithm development, and software engineering, with a passion for solving complex problems using cutting-edge technologies.

Key Responsibilities:

  • Model Development: Build, train, and optimise machine learning models for predictive analytics, natural language processing, computer vision, and other AI applications
  • Data Engineering: Preprocess, clean, and structure large datasets for model training and evaluation
  • Deployment & Integration: Implement ML models into production systems using scalable frameworks and APIs
  • Research & Innovation: Stay updated with the latest AI/ML trends and incorporate best practices into solutions
  • Performance Monitoring: Continuously monitor and improve model accuracy, efficiency, and robustness
  • Collaboration: Work closely with cross-functional teams including software engineers, product managers, and data analysts to deliver AI-driven features

Required Skills & Qualifications:

  • Bachelor’s or Master’s degree in Computer Science, Data Science, or related field
  • Strong proficiency in Python, TensorFlow, PyTorch, and ML libraries
  • Experience with data pipelines, feature engineering, and model deployment
  • Knowledge of cloud platforms (Azure, AWS, GCP) and containerisation (Docker, Kubernetes)
  • Familiarity with MLOps practices and CI/CD pipelines
  • Excellent problem-solving and analytical skills

Preferred Skills:

  • Experience with deep learning, transformer models, and generative AI
  • Understanding of big data technologies (Spark, Hadoop)
  • Exposure to AI ethics, bias mitigation, and responsible AI practices

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