ML Engineer

Job Overview

Kalibri Labs is looking for a Machine Learning Engineer who will lead efforts to enhance the scalability and efficiency of data science solutions for our products. You’ll focus on productionizing machine learning applications and systems at scale. The ideal Machine Learning Engineer has applied expertise in Python, Scala, architecture and technical design at scale. This individual can deliver high quality solutions when provided with machine learning models and updates to those models. The individual is an excellent communicator and is comfortable describing solutions and findings to key stakeholders. The Machine Learning Engineer must write clean, performant, reusable code using existing and emerging technology in order to ensure high availability and performance of our machine learning applications. 

We are looking for an energetic team member with a desire to explore, innovate and drive industry disruptive change through next level insights of data analytics built on machine learning systems, modern deep learning techniques, and big data analytics. You’ll be working with massive data sets, constructing operational AI/ML architecture, and communicating key insights to our team of talented engineers to create industry leading products for our clients.

Responsibilities for Machine Learning Engineer

• Partner with stakeholders throughout the organization to identify opportunities for leveraging company data to ensure quality, scalability and efficiency of Data Science solutions
• Tune, operationalize, and deploy high quality AI/ML algorithms into Kalibri’s data platform
• Build systems that setup, generate, and organize training data for online and offline models. Design, develop, and deploy models that integrate within Kalibri’s ecosystem
• Work closely with data scientists to standardize, automate, and operate ML systems. 
• Coordinate with development and product functional teams to implement models and monitor outcomes
• Maintain and expand existing AWS/Snowflake infrastructure with industry best practices, considering scalability, reliability, quality, and cost
• Develop processes and tools to continuously monitor and analyze model performance and accuracy
• Build automated quality tests and monitors that ensure availability, consistency, and accuracy
• Participate in code reviews and design sessions within an Agile process paradigm

Knowledge Skills and Abilities

• 3+ years experience designing, building, and maintaining ML systems leveraging Data Science packages such as TensorFlow, scikit based packages, PyTorch etc, in a cloud based environment
• 2+ years of experience designing and implementing scalable systems and applications on cloud-based technologies
• Expert SQL and Python programming in a production context
• Experience owning a project across the full lifecycle to include design, development, deployment, and operations
• Strong background in modern data warehouse technologies such as Snowflake, Databricks, BigQuery
• SQL expertise in a modern data warehouse following an SQL-based ELT paradigm. Demonstrated ability to prepare for model deployment and integration into data pipelines with reactive, event-based systems
  • Confident working in container-based environments such as docker
  • Experience building ML pipeline using modern orchestration tools such as MLFlow, github actions, airflow, prefect, etc.
  • Bachelor’s Degree in Computer Science, Information Systems, or a related technical field, or equivalent work experience.