Azure AI/ML Engineer — Data Pipelines & MLOps

Analytica; Bethesda (MD); On-site; USD 120,000 - 180,000; Full time

Benefits offered by this job

Bonuses

Health insurance

Training funds

401k match

Job summary

Analytica in Bethesda, MD, seeks a mid-level AI/ML engineer to design, build, and deploy production-grade AI solutions on Azure. You will work hands-on with data engineers, analysts, and business stakeholders to turn complex data into actionable intelligence.

This role focuses on building scalable ML pipelines, applying software engineering practices across the AI lifecycle, and maturing cloud-native MLOps while advancing responsible AI practices.

Qualifications

• 3+ years of hands-on experience developing and deploying AI/ML solutions in production environments.
• Strong proficiency in Python with data analysis and ML libraries (Pandas, NumPy, scikit-learn, PyTorch, TensorFlow).
• Experience with Microsoft Azure data and AI services (Azure ML, ADLS Gen2, Synapse, Data Factory).
• Understanding of ML concepts, model lifecycle, training, validation, and evaluation.
• Experience with CI/CD and DevOps tools (Azure DevOps, GitHub).

Responsibilities

• Build, maintain, and optimize Azure-based data pipelines and AI workflows.
• Support data ingestion, transformation, enrichment, and dataset preparation for analytics and reporting.
• Implement data quality checks, schema management, and documentation.
• Develop and deploy NLP/ML workloads and secure data pipelines.
• Contribute to CI/CD, monitoring, and performance tuning in Agile teams.

Skills

Python (data analysis & ML libraries)

Azure cloud services

CI/CD & MLOps

Agile / DevOps

Education

Bachelor’s degree in CS/DS/Math or related

Tools

Azure Machine Learning

ADLS Gen2

Azure Synapse Analytics

Azure Data Factory

Python

Job description

Analytica in Bethesda, MD, seeks a mid-level AI/ML engineer to design, build, and deploy production-grade AI solutions on Azure. You will work hands-on with data engineers, analysts, and business stakeholders to turn complex data into actionable intelligence.

This role focuses on building scalable ML pipelines, applying software engineering practices across the AI lifecycle, and maturing cloud-native MLOps while advancing responsible AI practices.