AI/ML Engineer

We are looking for a skilled and motivated AI/ML Engineer with 2-5 years of experience to join our team
In this role, youll be instrumental in developing, deploying, and maintaining machine learning models and AI systems that solve real-world business challenges You will work closely with cross-functional teams to build innovative solutions from concept to delivery Education and Experience: A bachelors or masters degree in Computer Science, Data Science, or a related field Typically requires 2+ years of experience in AI/ML development Python: Strong proficiency in Python and its data science libraries (Pandas, NumPy, scikit-learn etc) ML/DL Frameworks: Hands-on experience with TensorFlow and/or PyTorch Google Cloud Platform: Deep knowledge of GCP services, particularly Vertex AI, BigQuery, Cloud Storage, Dataflow, and Kubernetes Engine (GKE) for containerized deployments Software Engineering: Solid understanding of software development principles, including Git for version control, RESTful APIs, and writing clean, maintainable code Excellent problem-solving and analytical skills Strong communication and teamwork abilities to work effectively in cross-functional teams A passion for continuous learning and staying current with the rapidly evolving fields of AI and cloud technology Develop and Deploy ML Models: Design, train, and evaluate machine learning and deep learning models using Python with frameworks like TensorFlow and PyTorch Youll be responsible for the entire model lifecycle, from data ingestion to deployment Utilize Google Cloud AI Services: Leverage a wide range of GCP AI and ML services to build solutions This includes using Vertex AI for model management, training, and serving, as well as pre-built APIs like the Vision AI API, Natural Language API, and Speech-to-Text API Data Engineering: Work with large datasets by using GCP data services such as BigQuery and Cloud Storage Youll build and manage data pipelines using tools like Dataflow or Dataproc to ensure data is clean, transformed, and ready for model training MLOps and Automation: Implement MLOps best practices to automate the machine learning workflow This involves creating CI/CD pipelines for models, monitoring model performance in production, and ensuring reproducibility Collaboration and Solution Design: Collaborate with data scientists, software engineers, and product managers to translate business needs into technical requirements Youll design end-to-end AI solutions that are scalable, cost-effective, and meet the companys goals Performance Optimization: Optimize models for performance, both in training and serving, by leveraging GCPs specialized hardware (like GPUs and TPUs) and distributed training capabilities