Day In The Life Of AWS Professional Services Consultant-Machine Learning Engineer

1. Title/ProServe team.

Machine Learning Engineer in the AWS Professional Services (ProServe) Shared Delivery Team (SDT) AI/ML Practice for North America.

2. Tell us about yourself.

In my life, I play many roles. Some of these are husband, father, engineer, consultant, teacher, and even small-scale farmer and gardener. As someone passionate about many things, I like to keep busy on many fronts.

Professionally, I love solving problems with data, math, and technology. My journey as a Machine Learning Engineer started with a joy for problem solving as a youth. This led me to pursue a career in engineering which began with my Bachelor’s degree from Purdue University in Electrical and Computer Engineering Technology. During and after college, I worked in a few different engineering roles: Instrumentation and Controls Engineer at an electrical utility provider, Hardware Verification Engineer at an automotive company, and Product and Test Engineer (PE/TE) at a semiconductor company. As a PE/TE, I discovered the power of using statistics to solve real world manufacturing problems and was hooked. Since I already loved coding, it made perfect sense to pursue a Masters in Data Science, which I obtained from Southern Methodist University, while continuing to work full time. This was the best professional decision I’ve ever made as it enabled me to solve unprecedented engineering and even marketing challenges as a PE/TE before leading to a career as a Data Scientist in supply chain, quality, manufacturing, and marketing domains. My career has since evolved to become a Machine Learning Engineer (MLE) at AWS where I get to help the world's biggest companies solve their toughest challenges by helping them design, develop, and productionize their end-to-end ML solutions!

Personally, I’ve become passionate about exploring the world of permaculture and homesteading to provide food for my family in the most healthy, natural, sustainable ways possible. I live on a small farm in Texas where I enjoy gardening with my family and raising chickens for meat and eggs. My family and I are excited to raise turkeys this year and are planning to raise pigs, goats, and cows as well. Outside this expanding passion for farm living, I also spend my time barbecuing, hiking, cycling, and mountain biking with members of my family and friends.

3. Tell us about your role.

As an MLE within AWS ProServe, opportunities to make significant impacts on our external customers, as well as internal teams and mechanisms, abound. My primary responsibilities are two-fold: 1) Solve our customer’s most challenging problems by designing and building their production-ready end-to-end AI/ML solutions, and 2) strengthen AWS’ ability to better serve its customers by educating our customers and my fellow consultants alike in various AI/ML and ML Operations (MLOps) techniques, AWS technologies, and best practices.

Many customers are witnessing the promise machine learning algorithms, architectures, and technologies provide to impact their businesses in powerful ways but find them challenging to scale to production capacity. That’s where AWS ProServe MLEs come in. We use both our data science and engineering experience to design and implement robust, highly-scalable, automated ML solution workflows for our customers. This encompasses everything from training and inference code optimization and containerization to executing MLOps strategies such as model versioning, model and data lineage tracking, model monitoring, model hosting and deployment, dynamic scaling, ML solution orchestration, and continuous learning. We further leverage DevOps best practices to streamline these solutions using continuous integration and continuous deployment (CI/CD) pipelines, infrastructure as code (IaC) for architecture, and containerization technologies such as Docker.

Since joining ProServe, I’ve had the pleasure of working projects in industries such as media entertainment, crop breeding science, healthcare, and major league sports. During these projects, I’ve helped design, architect, and implement dynamic ML solutions that use tools such as AWS Step Functions, Amazon SageMaker Pipelines, and Apache Airflow to orchestrate complex workflows that rely heavily on data and model lineage. I’ve also designed and built an end-to-end solution to extract features from and train a classification model on a large (8TB in size) dataset before serializing the model for batch inference. In these projects I’ve also coached customers in their adoption of SageMaker and SageMaker Studio and have created powerful, custom, production-ready ML solutions using tools such as Amazon Elastic Container Registry, AWS Batch, Amazon S3, Amazon DynamoDB, and AWS Step Functions to run inference on sports video clips at massive scale. My contributions have enabled customers to incorporate AI/ML into their business solutions to derive powerful insights, enjoy significant savings, use automation to rapidly accelerate their development lifecycle, and even improve the health of patients and athletes.

4. What is your typical day- Morning/noon/afternoon (Special activities, responsibilities, fun facts)?

I begin most days seeing my older kids off to school, completing morning animal chores, seeing my wife off to work, and getting an early jump start on my work day before my preschooler wakes up. I use this early morning time to prioritize internal initiatives such as improving my team’s MLE hiring mechanisms, generate intellectual content such as internal artifacts or external blogs, or research via on-demand training, AWS certification prep, blog/whitepaper review, or experimentation. After this, I prepare my preschooler for the day and drop him off at preschool.

I usually spend the rest of the day working on my customer project. I begin by tackling my sprint user stories which sometimes entail deeply focused coding sessions by myself, and other times technical, collaborative design meetings with other members of my customer team. After lunch, I attend the project’s daily standup meeting where I update my team members and customer on story progress, communicate roadblocks if I’ve identified any, share immediate next steps, and request team support if needed. The afternoon often consists of team collaboration, based on what was communicated by project team members during the standup. If not meeting synchronously over audio or video, much of this collaboration occurs asynchronously over Slack while continuing to knock out my user stories. Sometimes afternoons also include other internal team meetings to collaborate toward internal AWS/ProServe initiatives, interacting with AWS service teams to navigate challenging project obstacles, or mentoring new or junior consultants.

Once my work is finished and my family is home from work and school, we have dinner together. I then tend to evening animal chores, work in the garden, and put the kids to bed before relaxing prior to bed. These evening routines give me balance. They are an excellent way for me to leave the work day behind me and enjoy my time at home with my family.

5. What do you like most about working for AWS? Did you have any inspiring moments?

It’s too difficult to identify just one thing I enjoy most, so I’ll provide two.

The first thing I love is the rate at which I get to learn new things. AWS places so much emphasis on educating and developing oneself that I’ve grown faster during my time here than I had at prior companies. There are what feels like an infinite number of resources available to develop myself as an engineer, as a scientist, as a communicator, as a leader.... you name it. This is expanded even further in ProServe since I get to work with all types of customers across all sorts of industries and domains to solve very diverse problems. Before joining AWS, I could only dream of working for a company where learning and demonstrating curiosity plays such an important and valued role.

The second thing I love about working at AWS Proserve is the lifestyle it enables me to live. Because I’m able to work primarily from home, I find it easier to stay connected with my spouse and children. Since my team empowers me to be flexible in my daily work routine, I’m able to shuttle my preschooler to preschool and to be home when my school-age kids arrive home on the school bus - there is no problem with starting my work day earlier or ending later to accommodate. Working for AWS also allows me to enrich my life outside of work in ways I was unable to while working for other employers. Putting on my not-so-figurative farmer’s hat and getting my hands dirty at the end of each day and on the weekends is important to my long-term happiness and sustained success as a technologist. My ability to work remotely as a Machine Learning Engineer in AWS ProServe makes that possible.

6. What is your favorite leadership principle and why?

My favorite leadership principle is “Learn and Be Curious.” I love this principle and where it fits at AWS because it is not only a pleasure but also a necessity. I get to learn and grow so much everyday to better serve my customers. When I see my new knowledge or skills impact and delight customers, my thirst for more knowledge grows even greater. I’m so grateful AWS encourages and supports this learning model. And as with all the leadership principles, constant learning produces significant rewards outside of work as well.