AI/ML Software Engineer

Required Minimum Qualifications

2 years of related experience with a Bachelor’s degree in Computer Engineering, Computer Science, Information Technology, High Performance Computing, Analytics, Artificial Intelligence, Electrical Engineering, Aerospace Engineering, Civil Engineering or related AI research focused field

Strong understanding of AI development in cloud environments such as AWS, Azure, Google Cloud/Colaboratory

Experience with Scala, Spark, SQL, MATLAB, GitHub, Probability & Statistic and Machine Learning

Passion for working with data (can think in data structures)

Ability to work well with a variety of teams with diverse skill sets

Preferred Qualifications

Advanced degree in Data Science, Computer Science, Electrical Engineering, Aerospace Engineering, Civil Engineering or related AI field

Documented experience conducting advanced research in AI pipeline design using proven Data Science methodologies as well as System Engineering practices.

Fundamental programming skills in core data science languages such as Python, R, MATLAB and SQL

Experience modeling data in JSON, XML and Entity Relational Diagraming (ERD) design

Experience working with cloud-based analytics environment, such as Databricks, Azure ML and AWS

Conceptual development in RStudio, JupyterLab and/or Jupyter Notebooks

Experience working with databases (I.e. configuring, deploying, securing and maintaining)

Experience working with government systems

Experience writing research proposals and documenting results

Currently possess or can obtain a DoD security clearance up to TS/SCI

Key Responsibilities

Apply tools and frameworks to deploy machine learning models

Conduct data conditioning, processing, filtering, and fusion to support the creation of automated algorithms

Make decisions regarding implementation based on available hardware (e.g., embedded systems, cloud, server infrastructure)

Design application logging capabilities and perform debugging with available logs

Additional Responsibilities

Applying the Cross Industry Standard Process for Data Mining (CRIPS-DM)

Data parsing, preparation and fusing of disparate data sources using fundamental data python libraries and established practices and procedures

Independently conducting exploratory data analysis, documenting and presenting findings to senior leadership and decision makers

Applying data collection techniques in support of ETL and ELT

Database modeling, data design and application of stored procedures, stored functions and triggers

Application of Machine Learning techniques such as supervised, unsupervised and semi-supervised learning

Application of Natural Language Processing techniques

Present research at conferences, contribute to business development, and meet with customers