ML Solutions Architect Manager

Meaningful work you’ll be part of 

As an ML Solutions Architect Manager, you’ll work as part of a team of problem solvers, helping to solve complex business issues from strategy to execution. Responsibilities include but are not limited to: 

• Lead presales engagements and ongoing client interactions to define business needs and translate them into ML solutions. 

• Collaborate with cross-functional teams, including data scientists and engineers, to design, deploy, and integrate scalable time-series forecasting models. 

• Ensure ML solutions align with client business goals, driving actionable insights and enhanced decision-making. 

• Facilitate the integration of client transactional data into ML models, ensuring seamless data flow and accuracy. 

• Guide long-term data pipeline strategy to support sustainable and scalable ML implementations across client environments. 

Experiences and skills you’ll use to solve  

• Experience in designing and architecting ML solutions for time-series forecasting, with a focus on scalability, efficiency, and effectiveness, leveraging AWS services such as Amazon Forecast, SageMaker, Lambda, and other relevant cloud platforms like Microsoft Azure and Google Cloud. 

• Proven experience in developing complex statistical and mathematical models to analyze and predict temporal data patterns, ensuring high levels of accuracy and reliability.  

• Strong collaboration skills with data engineers and scientists to preprocess, clean, and structure large datasets for time-series analysis.  

• Expertise in facilitating the implementation of state-of-the-art ML pipelines, algorithms, and statistical methods for forecasting, optimizing for performance and computational efficiency.  

• Working knowledge of enterprise architecture design, data structures (data warehousing, data lakes, data models) and the different components of an organizations technology landscape (tools and systems by functional areas). 

• Demonstrated experience in presales activities and client engagement, with the ability to communicate effectively about ML model outputs and their business implications.  

• Proficiency in programming languages such as Python, R, and Spark, and familiarity with ML libraries, frameworks, SQL, and experience with relational databases.  

• Experience in orchestrating ML pipelines using ML Flow, KubeFlow, or similar ML Ops platforms.  

• Ability to guide the integration of ML models into production environments, ensuring seamless deployment and scalability on the AWS cloud and extending capabilities into other cloud platforms.  

• Commitment to staying abreast of the latest advancements in ML, time-series forecasting, and leading technologies, continuously improving our solutions and methodologies.