Ai Ml Engineer

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., H2O.ai, 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.