Ai Ml Engineer

3 days ago

Ahmedabad, Gujarat, India Pacific Global Solutions Full time ₹ 8,00,000 - ₹ 24,00,000 per year

Role & responsibilities :

  • Design, develop, and implement machine learning models and algorithms to address business challenges.
  • Work with data scientists, engineers, and stakeholders to understand requirements and translate them into technical solutions.
  • Perform data preprocessing, feature engineering, and exploratory data analysis on large datasets.
  • Train, validate, and deploy machine learning and deep learning models using frameworks such as TensorFlow, PyTorch, or scikit-learn.
  • Optimize model performance, including hyperparameter tuning and debugging.
  • Develop and maintain APIs and pipelines to integrate ML models into production systems.
  • Stay updated with the latest advancements in AI/ML research and incorporate relevant technologies into ongoing projects.
  • Create technical documentation for models, processes, and systems.
  • Collaborate with cross-functional teams to deliver end-to-end AI/ML solutions.

Required Skills:

  • Strong proficiency in Python, including libraries such as NumPy, Pandas, and scikit-learn.
  • Hands-on experience with machine learning frameworks like TensorFlow, PyTorch, or Keras.
  • Solid understanding of supervised, unsupervised, and reinforcement learning algorithms.
  • Experience with NLP (Natural Language Processing), Computer Vision, or time-series analysis, Predictive Analytics.
  • Proficiency in data preprocessing, feature selection, and model evaluation techniques.
  • Familiarity with cloud platforms (e.g., AWS, Azure, Google Cloud) and MLOps tools.
  • Experience with distributed computing tools like Hadoop or Spark.
  • Strong knowledge of RESTful API design and model deployment strategies (e.g., Flask, FastAPI, or Docker).
  • Excellent problem-solving skills and ability to work independently or collaboratively.

Preferred candidate profile :

  • Experience with big data tools like Hadoop, Spark, or Kafka.
  • Exposure to database systems like SQL, NoSQL, or Graph Databases.
  • Knowledge of AutoML tools (e.g., , DataRobot) or low-code platforms.
  • Understanding of edge AI and deploying models on embedded devices.
  • Familiarity with containerization tools like Docker and orchestration systems like Kubernetes.
  • Knowledge of DevOps and CI/CD pipelines for deploying machine learning models.

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