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Designs and optimizes scalable data pipelines, machine learning models, and AI-enabled enterprise applications. Builds agentic systems, chatbots, autonomous agents, and automation tools; integrates data and AI solutions with business platforms; and ensures data quality, security, compliance, and governance. The role requires Python, SQL, cloud data platforms, modern pipeline tools, MLOps, API integration, and collaborative delivery of reliable end-to-end solutions.
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Role Summary
As a Data & AI Engineer, you will design, build, and optimize data pipelines, machine learning models, and AI-enabled solutions that empower enterprise teams to make smarter decisions and automate complex workflows. You will play a key role in delivering agentic capabilities by developing intelligent applications, autonomous agents, and advanced automation. You will collaborate with business and technical teams to create reliable, scalable, and secure data and AI solutions that integrate seamlessly with enterprise platforms.
Key Responsibilities
- Design and build scalable data pipelines to collect, clean, and organize data from various sources using modern tools.
- Integrate data and AI solutions with business applications and enterprise platforms.
- Develop and deploy machine learning models, for enterprise use cases.
- Strong knowledge of data architecture, data engineering, data modeling, data warehousing, and data platforms.
- Ensure data quality, security, and compliance across all data and AI projects, following best practices and governance frameworks.
- Create intelligent applications such as chatbots and automation tools integrated with enterprise platforms.
- Collaborate with cross-functional teams to deliver end-to-end solutions.
- Document processes and share technical knowledge to support team learning and project continuity.
- Apply advanced data engineering and machine learning techniques to solve complex business problems and optimize data architectures.
- Contribute to agentic AI initiatives, such as autonomous agents, advanced chatbots, and AI-driven automation platforms.
Minimum Qualifications
- Bachelor’s degree in Computer Science, Engineering, Mathematics, or a related field, or equivalent practical experience.
- Minimum 3 years of hands-on experience in data engineering, machine learning engineering, or AI solution development.
- Proficiency in SQL for data querying, transformation, and analysis.
- Strong programming skills in Python for data processing, automation, and model development.
- Experience building and maintaining data pipelines using modern tools (e.g., dbt, Fivetran, Airflow).
- Experience with cloud data platforms such as Snowflake, Databricks, BigQuery, Redshift, or Synapse.
- Experience with at least one major cloud provider (AWS, GCP, or Azure).
- Practical experience working with machine learning concepts, including model training, evaluation, and deployment.
- Exposure to MLOps practices such as model packaging, CI/CD, and monitoring.
- Experience integrating data and AI solutions with business applications or enterprise platforms.
- Experience with API integration for connecting data, AI models, and applications.
- Ability to develop and support intelligent applications (e.g., chatbots, automation tools).
- Knowledge of data quality assurance, security, and compliance best practices.
- Experience documenting processes and sharing technical knowledge with team members.
- Strong problem-solving skills and ability to work collaboratively in cross-functional teams.
- Effective written and verbal communication skills.
Preferred Qualifications
- Master’s degree or higher in a relevant field.
- Advanced experience in data engineering, including building scalable, high-performance data architectures and ETL frameworks.
- Advanced experience in machine learning, including designing, training, and deploying complex models for real-world applications.
- Experience with agentic AI systems, such as autonomous agents, advanced chatbots, or AI-driven automation platforms.
- Experience implementing and maintaining data governance frameworks, including data lineage, metadata management, privacy, and compliance.
- Experience with containerization (Docker) and modern software development practices.
- Exposure to vector databases, semantic search, or feature stores.
The base pay range for this position varies based on the geographic location. More information about the pay range specific to candidate location and other factors will be shared during the recruitment process. Individual pay is determined based on location of residence and multiple factors, including job-related knowledge, skills and experience.
San Francisco Bay Area:
89,700 - 143,800 USD AnnualSelect US Metros and States:
78,000 - 125,000 USD AnnualOther US Locations:
70,200 - 112,500 USD AnnualAs a part of the total compensation package, this role may be eligible for a bonus. For information on our benefits click here.
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With a business-friendly climate and research universities like CU Boulder and Colorado State, Colorado has made a name for itself as a startup ecosystem. The state boasts a skilled workforce and high quality of life thanks to its affordable housing, vibrant cultural scene and unparalleled opportunities for outdoor recreation. Colorado is also home to the National Renewable Energy Laboratory, helping cement its status as a hub for renewable energy innovation.
Key Facts About Colorado Tech
- Number of Tech Workers: 260,000; 8.5% of overall workforce (2024 CompTIA survey)
- Major Tech Employers: Lockheed Martin, Century Link, Comcast, BAE Systems, Level 3
- Key Industries: Software, artificial intelligence, aerospace, e-commerce, fintech, healthtech
- Funding Landscape: $4.9 billion in VC funding in 2024 (Pitchbook)
- Notable Investors: Access Venture Partners, Ridgeline Ventures, Techstars, Blackhorn Ventures
- Research Centers and Universities: Colorado School of Mines, University of Colorado Boulder, University of Denver, Colorado State University, Mesa Laboratory, Space Science Institute, National Center for Atmospheric Research, National Renewable Energy Laboratory, Gottlieb Institute
