Position: MLOps
Company: Make it in Ukraine
Experience required: 3y
Category: Data Science
English level: upper
Published: 2022-04-01T00:00:00+03:00

Over time, our mission is to become the all-in-one spend management solution for SMBs that uses AI to: analyze spend, manage approval and payment workflows, identify anomalous spend and savings opportunities, benchmark spend performance vs. peers, find and negotiate savings with vendors, and forecast future expense trends.
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ABOUT THIS ROLE
We’re looking to build the next billion-dollar business in enterprise SAAS. We have an amazing founding team already, with experts in product, engineering, data science, and machine learning. We are now looking for a second MLOps engineer that will own the build and maintenance of our data and ML pipelines. At the core of our product is invoice digitization to
transform invoice data, currently locked away in PDFs, into a rich and clean digital data asset -
- and your role is critical to making this a reality!
Here are some of the major items you will own at our company:
● Perform inference using existing NLP and computer vision models
● Use human-in-the-loop annotations to automatically retrain and deploy updated
models
● Monitor and maintain the models in production over time
● Refactor code form data scientists to push into production
● Architect and deliver new pipelines to enrich invoice data from our machine learning models
● Make models available as APIs
You will work closely with the data scientists to productionize the data science and machine learning code that is developed, and you will work closely with the engineering team to make sure the data and ML engineering pipelines are performant in production. Eventually, the data
and ML engineering work will also require tight integrations into other fintech platforms (e.g., payment platforms).
You’ll be accountable for us:
● Data and ML engineering vision – ownership over and definition of the data and ML engineering pipeline, working closely with the executive team, and acting as a thought leader across the company
● Technical execution – define, articulate, and execute the data and ML engineering strategy from launch to scale, managing the day-to-day execution, and implementing best practices
● Engineering culture - champion agile and design philosophy, CI/CD, test-driven development, thoughtful architecture, empathetic communication, and measurability
● Getting your hands dirty - we’re a small team and we have a lot of work ahead of us.
You should be excited to roll up your sleeves and help the team in any way you can
● Build, build, build – jumping in and writing code, from architecture to fixing bugs, delivering the MVP within the first 3 months of hire. We'll rely on you and your team to handle day-to-day execution including:
▪ Shipping highly scalable distributed systems on cloud platforms (AWS/GCP) and database technologies (SQL/NoSQL/column-oriented data
stores/distributed databases)
▪ Developing Big Data systems (Spark/Airflow) and microservice architectures
▪ Collecting, storing, processing, and analyzing structured and unstructured data and implementing ETL processes
▪ Wrangling, standardizing, enhancing, implementing, and monitoring data
repositories
▪ Creating workflows to ingest, enrich, and make data available across the Glean platform
▪ Defining data retention policies and automating data delivery
PERFECT FIT
You’ll be a perfect fit for the our Team if…
● You want to join an early-stage startup or you are extremely anxious to be challenged
at your first startup
● You like the tension between craft and shipping. You have strong ability to quickly and
effectively evaluate technical tradeoffs and translate them into short/long term
business decisions
● You’ve built highly scalable data and ML pipelines and have deep expertise in setting
technical vision, architecting, building, and maintaining performant systems, from
launch to scale
● You are passionate about building data and ML engineering teams and rapidly
developing data and ML pipelines at various stages of growth
● You pride yourself in communicating complex concepts, including the ability to distill
intricate workflows and systems into clear processes and decisions with measurable
company-wide impact
● You ask “why” a lot and use critical thinking and data to back up your intuitions. You
hate when a customer struggles through your product experience
● You have managed a budget before and have seen first-hand the challenges in
managing vendor spend
● You are a pro at Python; proficiency at any static type language is a plus.