Analytica is seeking a mid-Level AI-ML Engineer to join our growing data and AI team and help design, build, and deploy production-grade AI solutions that make a real-world impact. In this role, you will work hands-on with modern Microsoft Azure services and AI/ML technologies to turn complex data into actionable intelligence. You’ll collaborate closely with data engineers, analysts, and business stakeholders to deliver scalable, secure, and responsible AI solutions that move from experimentation into daily operations.
This is an excellent opportunity for an engineer who enjoys working across the full AI lifecycle using a reusable software development approach across rom data preparation and model development to using software engineering principles to deployment, monitoring, and continuous improvement—while growing their expertise in cloud-native AI and MLOps practices.
Analytica has been recognized by Inc. Magazine as one of the fastest-growing 250 businesses in the US for 3 years. We work with U.S. government clients in health, civilian, and national security missions to build better technology products that impact our day-to-day lives. The company offers competitive compensation with opportunities for bonuses, employer-paid health care, training and development funds, and 401k match.
Key Responsibilities:
Build, maintain, and optimize Azure-based data pipelines and workflows using Azure Data Factory, ADLS Gen2, Synapse Analytics, Azure SQL, Python, and SQL.
Support batch and near–real-time data ingestion, transformation, enrichment, and aggregation to deliver analytics- and reporting-ready datasets.
Implement data quality checks, schema management, and validation to ensure reliable, trusted, and well-documented data.
Produce architecture artifacts, test plans, security documentation, and operational runbooks per sprint requirements.
Contribute to CI/CD pipelines, monitoring, troubleshooting, and performance tuning to ensure reliable data operations.
Work within Agile teams, collaborating closely with senior engineers, analysts, and stakeholders to deliver well-defined data requirements.
Required Experience & Qualifications:
Strong proficiency in Python, including data analysis and ML libraries (e.g., Pandas, NumPy, scikit-learn, PyTorch, TensorFlow).
Solid understanding of machine learning concepts, model selection, training, validation, and evaluation techniques.
Working knowledge of MLOps concepts, including CI/CD pipelines, version control, and model lifecycle management.
Bachelor’s degree in Computer Science, Data Science, Engineering, Mathematics, or a related field (or equivalent practical experience).
Desired / Preferred Experience:
Knowledge of responsible AI practices, including fairness, explainability, model governance, and security considerations.
