Data and Machine Learning Engineer

Job Description Job Description We are seeking a highly motivated and skilled Data and Machine Learning Engineer to join our team. You will be responsible for the entire lifecycle of machine learning projects, from data acquisition and cleaning to model deployment and monitoring. You will work closely with data scientists, software engineers, and product managers to develop and implement innovative solutions that leverage machine learning to solve real-world problems.

Responsibilities: Train, evaluate, and optimize machine learning models on Google Cloud Platform Design, develop, and implement machine learning models and pipelines. Collaborate with data scientists to understand business requirements and translate them into technical specifications. Acquire, clean, and prepare data for machine learning models. Develop and maintain machine learning infrastructure, including cloud platforms and distributed computing frameworks. Monitor and maintain deployed models, ensuring their accuracy and performance. Document and communicate technical concepts to both technical and non-technical audiences. Stay up-to-date with the latest advancements in machine learning and related technologies.

Qualifications: Bachelor’s degree in Computer Science, Statistics, Mathematics, or a related field (or equivalent experience). Strong experience in programming languages such as Python, R, and Java. Experience with machine learning libraries and frameworks (e.g., TensorFlow, PyTorch, scikit-learn). Experience with data wrangling, cleaning, and manipulation techniques. Experience with GCP Products – BigQuery, Vertex AI and Gen AI Platform.

Excellent problem-solving and analytical skills. Strong communication and collaboration skills. Ability to work independently and as part of a team. Desired Skills: Experience with deep learning models. Experience with distributed computing frameworks (e.g., Spark, Hadoop). Experience with model deployment and monitoring tools. Familiarity with software engineering best practices.