AI/ML Engineer

Role Description

As an AI/ML Engineer, you will be responsible for developing and implementing AI/ML solutions to solve complex business problems. You will work closely with cross-functional teams to understand requirements, design and develop AI/ML models, and deploy them into production. We are looking for someone with a strong knowledge of ML, Knowledge Graphs, Graph Algorithms, Deep Learning, and experience working with massive amounts of data. You should also have strong software engineering skills and the ability to build systems that reach companies scale.

Key Responsibilities

• Develop and implement AI/ML models and algorithms to solve business problems.
• Collaborate with cross-functional teams to understand requirements and translate them into technical solutions.
• Design and develop data pipelines to preprocess and transform data for AI/ML models.
• Train and evaluate AI/ML models using large datasets.
• Optimize and fine-tune AI/ML models for performance and accuracy.
• Deploy AI/ML models into production environments.
• Maintain deployed models, ensuring their performance and reliability.
• Stay up-to-date with the latest advancements in AI/ML technologies and techniques.
• Design graph data models specifically for algorithm optimization.
• Design and add the data from the physical and logical infrastructure components and their relationships.
• Develop and implement data ETL pipelines within AWS & Azure

Qualifications/Skills

• Bachelor's or Master's degree in Computer Science, Engineering, or a related field.
• 5-7 years of demonstrated experience in applied AI/ML engineering.
• Strong programming skills in Python, with experience in developing and maintaining production-level code.
• Experience with designing and implementing graph databases, such as Amazon Neptune, TigerGraph.
• Proficiency in working with large datasets and data preprocessing.
• Solid understanding of AI/ML algorithms and techniques, including deep learning, reinforcement learning, and natural language processing.
• Familiarity with AI/ML libraries and frameworks, such as TensorFlow, PyTorch, scikit-learn, and Keras.
• Experience in creating infrastructure graph data models
• Experience with cloud platforms, such as AWS or Azure, for deploying and scaling AI/ML models.
• Experience with ETL tools such as Airflow, and Jenkins.
• Strong problem-solving and analytical skills.
• Excellent communication and collaboration skills.
• Knowledge of infrastructure operations
• Experience with distributed computing frameworks, such as Apache Spark.
• Knowledge of graph-based AI/ML algorithms and techniques.
• Familiarity with DevOps practices for AI/ML model deployment and monitoring.