General Summary:

The Machine Learning Engineer is responsible for supporting the business by developing Integrated AL/ML solutions for use cases across the company. This position will develop and deploy AI/Machine Learning models in GCP, Azure Open AI or other cloud platform that drives business value, automates processes and enables efficiency. Machine Learning Engineer is an expert at working with structured and non-structured data sources and other programming/scripting languages. His/her expertise will be at the intersection of programming, mathematics, and data science. The role requires strong programing background and analytical mindset to collect requirements and translate them into an analytical solution. This person needs to have the ability to ask the right questions and translate complex technical details into business language for leadership. Ideal candidates enjoy working in demanding and fast-paced environments and are proactive, goal-oriented and team players.

 

Reports To: VP, Enterprise Data Analytics & AI

 

Essential Duties and Responsibilities:

Design, build, and fine-tune machine learning models. Work with large datasets to train algorithms. Preprocess data, select appropriate models, and optimize their performance. Develop and deploy machine learning and AI models in cloud platform to enable and support strategic priorities of DBI across departments. Collaborate with stakeholders across organization to understand requirements, assimilate data needed, build predictive/prescriptive models, communicate results, and deploy predictions on edge or central database system for real time or batch predictions. Developing and implementing appropriate RAG architecture for model deployment. Working closely with cloud data engineers to implement RAG effectively within cloud platform for efficient inference. Staying current with generative AI enhancements and foundational models being available in the market and doing R&D on applicability on DBI use cases. Build an AI infrastructure and set standards for leveraging available cloud platforms to enable and educate citizen data scientists to leverage the power of machine learning, Build self-serve AI framework. Develop a strategic working relationship with cross-functional teams to align the data solutions to business KPI’s, levers and goals. Drive cross-functional teams to meet business objectives and influence co-workers and stakeholders, fostering strong working relationships. Ability to stand by decisions and move forward with courage. Lead several concurrent initiatives/projects to develop custom, easy-to-use self-service solutions to serve multiple stakeholder groups and business objectives. Furnish analysis with analytical insight while maintaining completeness, accuracy, and documentation. Identify areas and define solutions to maximize value-add to the organization

 

Required Skills:

Strong analytical skills with the ability to collect, organize, analyze, and disseminate significant amounts of information with attention to detail and accuracy using descriptive and predictive analytics that optimize statistical efficiency and quality of the model – feature engineering, feature selection, model evaluation, model selection, model drift evaluation etc. Strong Knowledge of supervised and unsupervised deep learning algorithms and experience in advanced deep learning libraries like TensorFlow, Keras, Pytorch, Caffe, Theano etc. Experience leveraging foundational models like Claude 3.5, GPTs, Gemini, Llama etc. Knowledge and experience in implementing ethical and responsible AI principles while implementing ML solutions is preferred. Strong statistical knowledge and understanding of application of algorithms based on data and business need. Extensive background in statistical analysis, computational sciences, mathematics, physics, or econometrics. Advanced analytical and problem-solving skills and an ability to work independently in a fast-paced and rapidly changing environment. Entrepreneurial spirit - self-motivated, strong sense of ownership/accountability, and results oriented with the ability to manage time and schedules effectively.

 

Competencies:

SETTING GOALS – Creates and follows effective plans. Anticipates risks, creates contingency plans. Aligns plans with goals. Allocates adequate resources. Accepts and supports change. Willing to take risks and suggests new ideas, approaches. Takes initiative. Seeks out learning activities.

 

WORKING WITH OTHERS – Clearly articulates own, other’s goals. Promotes a team atmosphere by demonstrating humility and respect. Builds effective relationships, relates well to others. Delivers and responds to feedback in a constructive manner. Considers multiple perspectives. Handles conflict, pressure, uncertainty and adapts independently. Meets commitments. Dedicated to working with business partners on their expectations.

 

GETTING RESULTS – Personally accountable for work performance targets and achieving results. Prioritizes well. Anticipates and handles obstacles effectively. Makes good, timely decisions. Can simplify and process complex problems. Understands underlying issues and addresses root causes. Meets deadlines, works until finished.

 

Qualifications:

Experience:

Master’s degree in analytics or computer science with 4+ years of experience developing and deploying machine learning models is required Experience with Big Data environments (very large 1st party data integrating multiple internal and external disjointed data) Experience in model metrics monitoring, orchestrating ML workflows using pipelines, version control, model drift management. Experience working with underlying databases and tables. Understands table relationships to be able to do dimensional modeling; understands lookup tables and fact tables. Ability to write reusable code components Familiarity with programming languages such as R and/or Python/ Julia/ Scala Communication skills including the ability to identify and communicate data driven insights

 

Preferred Qualifications:

Experience in development and deployment of models in GCP including Vertex AI OR Azure Open AI Experience in SQL for data mining, feature engineering. Experience with cloud infrastructure platforms (Google Cloud Platform) is preferable PhD in statistical domain/topic Retail experience preferred.

 

Education:

Master’s degree in Analytics or other STEM field (Data Analytics, Data Science, Computer Science, Data Engineering, Applied Mathematics)

 

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