Today
Public Trust
$115,000 - $125,000
Unspecified
Construction/Facilities
Woodlawn, MD (On-Site/Office)
Piper Companies is seeking a AI/Machine Learning Ops Engineer to join a mission-critical forward team in Woodlawn, MD.
Responsibilities for the Machine Learning Ops Engineer include:
· Design and deploy scalable ML pipelines using Python and modern frameworks (e.g., TensorFlow, PyTorch, Transformers).
· Lead NLP and machine learning initiatives , applying statistical modeling and domain knowledge to solve complex business problems.
· Collaborate across teams , providing technical guidance and fostering a high-performing, motivated work environment.
· Utilize SQL/PostgreSQL and NoSQL databases (e.g., MongoDB) for data processing and model integration.
· Leverage cloud platforms and big data tools (AWS, Azure, Spark, Hadoop) to support AI-driven solutions.
Qualifications for the Machine Learning Ops Engineer include:
• Master's Degree + 5 years' relevant experience OR Bachelor's degree + 7 years of experience
• Minimum Public Trust clearance or eligibility is required .
• Expert proficiency in the following:
o Programming Languages: Python (Expert)
o Databases: PostgreSQL (Expert), MongoDB
o ML Frameworks & Libraries: TensorFlow, PyTorch, Transformers, Scikit-learn, XGBoost, Keras, Pandas
o NLP Techniques: Information Extraction, Semantic Parsing, Chunking/Tokenization, Pattern Recognition
o Model Types: BERT, CNN, RNN, LSTM, SVMs, k-NN, Regression, Classification, Ensemble Methods, Graphical Models, Clustering
o Tools & Technologies: Git, Tesseract, Regular Expressions
o Statistical Modeling: Classification, Feature Extraction, Sparse Data AnalysisPreferred skills
o Cloud platforms: AWS, Azure, GCP
o Web service technologies: SOAP, WSDL, WS-Security
o NoSQL databases: DB2, Oracle, MySQL, HBase
o Big Data technologies: Hadoop, Spark, HDFS, MapReduce, YARN, Scala, PySpark
o XML and related technologies: XSD, XPath, XSLT
o Messaging systems: IBM MQ Series
o ebXML and other enterprise integration standards
Compensation for the Machine Learning Ops Engineer includes:
This job opens for applications on 9/29/25. Applications for this job will be accepted for at least 30 days from the posting date.
AI Engineer, Machine Learning Ops, MLOps, NLP, Natural Language Processing, Python, TensorFlow, PyTorch, Transformers, Scikit-learn, XGBoost, Keras, Pandas, CNN, RNN, LSTM, BERT, SVM, k-NN, Regression, Classification, Ensemble Methods, Graphical Models, Clustering, Feature Extraction, Sparse Data Analysis, Information Extraction, Semantic Parsing, Chunking, Tokenization, Pattern Recognition, Git, Tesseract, Regular Expressions, PostgreSQL, MongoDB, SQL, NoSQL, DB2, Oracle, MySQL, HBase, AWS, Azure, GCP, Spark, Hadoop, HDFS, MapReduce, YARN, Scala, PySpark, SOAP, WSDL, WS-Security, XML, XSD, XPath, XSLT, IBM MQ Series, ebXML, cloud platforms, big data, statistical modeling, scalable ML pipelines, model integration, public trust clearance, remote work, U.S. citizen, enterprise integration, mission-critical, cross-functional collaboration, technical guidance, high-performing teams.
#LI-SF1 #LI-ONSITE
Responsibilities for the Machine Learning Ops Engineer include:
· Design and deploy scalable ML pipelines using Python and modern frameworks (e.g., TensorFlow, PyTorch, Transformers).
· Lead NLP and machine learning initiatives , applying statistical modeling and domain knowledge to solve complex business problems.
· Collaborate across teams , providing technical guidance and fostering a high-performing, motivated work environment.
· Utilize SQL/PostgreSQL and NoSQL databases (e.g., MongoDB) for data processing and model integration.
· Leverage cloud platforms and big data tools (AWS, Azure, Spark, Hadoop) to support AI-driven solutions.
Qualifications for the Machine Learning Ops Engineer include:
• Master's Degree + 5 years' relevant experience OR Bachelor's degree + 7 years of experience
• Minimum Public Trust clearance or eligibility is required .
• Expert proficiency in the following:
o Programming Languages: Python (Expert)
o Databases: PostgreSQL (Expert), MongoDB
o ML Frameworks & Libraries: TensorFlow, PyTorch, Transformers, Scikit-learn, XGBoost, Keras, Pandas
o NLP Techniques: Information Extraction, Semantic Parsing, Chunking/Tokenization, Pattern Recognition
o Model Types: BERT, CNN, RNN, LSTM, SVMs, k-NN, Regression, Classification, Ensemble Methods, Graphical Models, Clustering
o Tools & Technologies: Git, Tesseract, Regular Expressions
o Statistical Modeling: Classification, Feature Extraction, Sparse Data AnalysisPreferred skills
o Cloud platforms: AWS, Azure, GCP
o Web service technologies: SOAP, WSDL, WS-Security
o NoSQL databases: DB2, Oracle, MySQL, HBase
o Big Data technologies: Hadoop, Spark, HDFS, MapReduce, YARN, Scala, PySpark
o XML and related technologies: XSD, XPath, XSLT
o Messaging systems: IBM MQ Series
o ebXML and other enterprise integration standards
Compensation for the Machine Learning Ops Engineer includes:
- Salary Range: $115,000- $125,000 **depending on experience*
- Full Benefits: Comprehensive benefits package (Healthcare, Dental, Vision, 401k, Paid Time Off, and Sick Leave (if required by law)
This job opens for applications on 9/29/25. Applications for this job will be accepted for at least 30 days from the posting date.
AI Engineer, Machine Learning Ops, MLOps, NLP, Natural Language Processing, Python, TensorFlow, PyTorch, Transformers, Scikit-learn, XGBoost, Keras, Pandas, CNN, RNN, LSTM, BERT, SVM, k-NN, Regression, Classification, Ensemble Methods, Graphical Models, Clustering, Feature Extraction, Sparse Data Analysis, Information Extraction, Semantic Parsing, Chunking, Tokenization, Pattern Recognition, Git, Tesseract, Regular Expressions, PostgreSQL, MongoDB, SQL, NoSQL, DB2, Oracle, MySQL, HBase, AWS, Azure, GCP, Spark, Hadoop, HDFS, MapReduce, YARN, Scala, PySpark, SOAP, WSDL, WS-Security, XML, XSD, XPath, XSLT, IBM MQ Series, ebXML, cloud platforms, big data, statistical modeling, scalable ML pipelines, model integration, public trust clearance, remote work, U.S. citizen, enterprise integration, mission-critical, cross-functional collaboration, technical guidance, high-performing teams.
#LI-SF1 #LI-ONSITE
group id: 10430981
