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AI-ML Engineer

Job in Bethesda, Montgomery County, Maryland, 20811, USA
Listing for: Analytica
Full Time position
Listed on 2026-10-05
Job specializations:
  • Software Development
    AI Engineer (Applied/Software), Data Engineering, Azure, Machine Learning/ ML Engineer
Salary/Wage Range or Industry Benchmark: 120000 - 180000 USD Yearly USD 120000.00 180000.00 YEAR
Job Description & How to Apply Below

Analytica isseeking amid-LevelAI-MLEngineerto 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 lifecycleusing areusablesoftware development approachacrossromdata preparation and model development tousing software engineering principles todeployment, monitoring, and continuous improvement—while growing their expertise in cloud-native AI andMLOpspractices.

Analytica has been recognized byInc. 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 thatimpactour day-to-day lives. The company offers competitive compensation with opportunities for bonuses, employer-paid health care, training and development funds, and 401kmatch.

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.

  • Build secure data pipelines, integrate structured/unstructured data, and support NLP/ML workloads.

  • Configure,monitor, and enhance enterprise AI capabilities across Azure and other approved platforms.

  • Ensure compliance with Federalarchitecture standards, EA guidelines, and security controls.

  • 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:
  • Us Citizen with the ability to secure public trust clearance

  • 3+ years of hands-on experience developing and deploying AI/ML solutions in production environments.

  • Strongproficiencyin Python, including data analysis and ML libraries (e.g., Pandas, Num Py, scikit-learn,PyTorch, Tensor Flow).

  • Solid understanding of machine learning concepts, model selection, training, validation, and evaluation techniques.

  • Experience working with Microsoft Azure services for data and AI workloads, such as

  • Azure Machine Learning

  • Azure Data Lake Storage (ADLS Gen2)

  • Azure Synapse Analytics (SQL and/or Spark)

  • Azure Data Factory

  • Working knowledge ofMLOpsconcepts, including CI/CD pipelines, version control, and model lifecycle management.

  • Experience collaborating in Agile development environments and using modern Dev Ops tools (e.g., Azure Dev Ops, Git Hub).

  • Bachelor’s degree in Computer Science, Data Science, Engineering, Mathematics, or a related field (or equivalent practical experience).

Desired /

Preferred Experience:
  • Microsoft Certified:
    Azure AI Engineer Associate

  • Experience with Copilot Studio or AI agent development, including orchestration and integration with APIs and data sources.

  • Familiarity with feature stores, vector databases, or retrieval-augmented generation (RAG) patterns.

  • Exposure to event-driven or streaming architectures (e.g.,…

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