AI/ML Engineer

1 week ago

Greater Chennai Area, India Mindsprint Full time

Job Description:

  • Develop, deploy, and optimize AI/ML models on Google Cloud Platform (GCP) to address complex business challenges.
  • Collaborate with cross-functional teams, including data engineers and data scientists, to design and implement scalable AI solutions.
  • Implement and manage machine learning workflows using GCP services such as AI Platform, BigQuery ML, and TensorFlow.
  • Integrate machine learning models into existing systems, ensuring they are production-ready and scalable.
  • Perform data preprocessing, feature engineering, and model evaluation to ensure high-quality model outputs.
  • Utilize cloud-native tools to automate AI/ML processes, including model training, deployment, and monitoring.
  • Work closely with stakeholders to translate business requirements into AI solutions and provide actionable insights.
  • Continuously monitor and improve the performance of AI models, ensuring they meet the desired business outcomes.


Profile Description:

  • Experience: 5+ years of experience in AI/ML engineering with a strong focus on data science and cloud platforms, particularly GCP.
  • Skills: Proficiency in Python, TensorFlow, scikit-learn, and cloud-based ML services (AI Platform, BigQuery ML). Strong background in data science, including statistics, data analysis, and feature engineering.
  • Qualifications: Bachelor’s or master’s degree in computer science, Data Science, AI, or related field.
  • Competencies: Ability to design and implement end-to-end machine learning pipelines on GCP. Strong analytical skills and the ability to work in a collaborative, fast-paced environment.
  • Certifications: Google Cloud Professional Data Engineer or AI/ML certification preferred.
  • Knowledge: In-depth understanding of machine learning algorithms, cloud computing, and MLOps practices.
  • Soft Skills: Strong communication and problem-solving skills, with the ability to convey complex technical concepts to non-technical stakeholders.
  • Tools: Experience with version control systems (Git), CI/CD pipelines, and cloud-based data storage solutions (e.g., BigQuery, Cloud Storage).

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