ML Architect

Overview of Role As the ideal ML Architect, you are someone who can do a deep technical dive and communicate effectively with others. You love wrangling messy data into an elegant solution, and helping others understand the power of their data. This role is a chance to have a huge impact on how businesses operate and make decisions on a daily basis. Responsibilities Dive deep into a wide range of data (tabular, text, image, etc.) to identify pain-points and deliver data-driven insights to our clients. Utilize analytical techniques to determine areas of opportunities to help meet business goals. Leverage GCP services to train and deploy off-the-shelf (Vertex AI/AutoML/BQML) or custom models to address a client s business problem. Be a thought leader for your team on projects and contribute to key practice initiatives. Act as subject matter expert on pre-sales calls and requests for proposals. Drive successful delivery of analytics solutions. Create reports and presentations that showcase the value of your solution to our clients. Work with other members of the data science team to establish a center of excellence and create standard operating procedures, accelerators, and sales assets. Lead the design of technical solutions to meet the business needs of customers and clients. This includes working with stakeholders to understand their requirements, developing high-level and detailed solution designs, and ensuring that the solutions are technically feasible and cost-effective. Provide technical leadership to customer and client engagements, including working with the sales team to develop and deliver technical presentations and proposals, and working with the delivery team to implement and support the technical solutions. Mentor and train junior team members and help them to develop their technical skills and knowledge. This includes providing feedback, coaching, and guidance. Qualifications 8+ years of experience with data science and 3+ years of relevant architectural experience. Python programming, R is a plus. SQL Experience. GenAI experience in one or all of Prompt Engineering, RAG, Fine-tuning is a must to have, Agentic AI is a plus to have. Knowledge of Data Warehouses (Snowflake, BigQuery, Redshift, Synapse). Experience with version control systems, such as Git/Github. Big Data processing framework experience (eg Spark/Pyspark, Dask, Hive). Experience developing Machine Learning models and using Deep Learning frameworks like Tensorflow, Keras, PyTorch. Ability to communicate complex, technical processes to non-technical business stakeholders. Experience developing and maintaining solution design standards and best practices. Ability to track changing business requirements and deliver quality solutions both independently and with teams of varying skill sets. Bachelor s degree in Data Science, Computer Science or similar. Experience with Google Cloud Platform is a plus.