Quanteon Solutions
Linkedin · Posted 1mo ago
AI / ML Engineer
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Indexed description
Responsibilities
- Develop and deploy machine learning, deep learning, and NLP models for various business use cases.
- Build end-to-end ML pipelines including data preprocessing, feature engineering, training, evaluation, and production deployment.
- Optimise model performance and ensure scalability in production environments.
- Work closely with data scientists, product teams, and engineers to translate business requirements into AI solutions.
- Conduct data analysis to identify trends and insights.
- Implement MLOps practices for versioning, monitoring, and automating ML workflows.
- Research and evaluate new AI/ML techniques, tools, and frameworks.
- Document system architecture, model design, and development processes.
- Strong programming skills in Python (NumPy, Pandas, Scikit-learn, TensorFlow, PyTorch, Keras).
- Hands-on experience in building, deploying, and fine-tuning ML/DL models in production.
- Good understanding of machine learning algorithms, neural networks, NLP, and computer vision.
- Experience with REST APIs, Docker, Kubernetes, and cloud platforms (AWS/GCP/Azure).
- Working knowledge of MLOps tools such as MLflow, Airflow, DVC, or Kubeflow.
- Familiarity with data pipelines and big data technologies (Spark, Hadoop) is a plus.
- Strong analytical skills and ability to work with large datasets.
- Excellent communication and problem-solving abilities.
- Experience in deploying models using cloud services (AWS Sagemaker, GCP Vertex AI, etc. ).
- Experience in LLM fine-tuning or Generative AI, Voice AI, is an added advantage.
- A bachelor's or master's degree in computer science, data science, AI, machine learning, or IT from a good college.
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