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Artificial Intelligence • Big Data • Healthtech • Information Technology • Machine Learning • Software • Analytics
Build and lead production AI/ML, Generative AI, and data engineering solutions for healthcare analytics and patient safety platforms. Develop full-stack applications, microservices, APIs, data pipelines, dashboards, self-service analytics, RAG systems, and event-driven architectures using cloud-native technologies. Apply engineering best practices including testing, CI/CD, observability, security, and performance optimization. Mentor engineers, lead technical initiatives, and collaborate with product, analytics, business, and platform teams.
Top Skills:
Amazon KinesisAWSAzureAzure Event HubsCi/CdDatabricksDelta LakeGCPGenerative AiJavaJavaScriptKafkaLarge Language Models (Llms)Microsoft FabricMlopsPysparkPythonReactRest ApisRetrieval-Augmented Generation (Rag)SnowflakeSparkSpring BootSQLTypescriptVector Search
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AdTech • Cloud • Digital Media • Information Technology • News + Entertainment • App development
Designs and operates governance, security, deployment, monitoring, and cost-management frameworks for enterprise AI platforms. The role builds semantic models in Snowflake and Databricks, develops CI/CD and infrastructure-as-code solutions, enables multi-environment deployments, monitors AI reliability and usage, and implements auditability, responsible AI standards, RBAC, identity controls, and policy enforcement. It also partners with data and engineering teams to manage lineage, governed metrics, model operations, and cloud consumption across AI workloads.
Top Skills:
AiopsSparkAWSAzure AdDatabricksDatabricks Agent BricksDatabricks GenieDbtDockerGenerative AiGitGithub ActionsIamInfrastructure As CodeKubernetesLanggraphMcpMlopsPythonRagRbacSnowflakeSnowflake CortexSQLTerraform
Cloud • Computer Vision • Information Technology • Sales • Security • Cybersecurity
Architect, build, deploy, and scale production AI agents and platforms for legal workflows. Develop secure, reliable, and cost-effective AI solutions; prototype and evaluate agents; optimize performance; redesign business processes; and collaborate with Legal, Data, Security, Privacy, and Engineering teams. Contribute to AI training, governance, responsible AI adoption, and enterprise standards while translating technical capabilities into measurable business outcomes.
Top Skills:
Ai AgentsAi/MlAWSGCPKnowledge GraphsLarge Language Models (Llms)Model Context Protocol (Mcp)Multi-Agent SystemsMultimodal Document ProcessingPythonSynthetic Data
Build and deploy production AI agents and MCP integrations that automate logistics workflows across dispatch, billing, compliance, and fleet operations. Develop multi-agent workflows, hybrid retrieval architectures using RAG, KAG, and CAG, LLM integration layers, and domain-specific AI tools. Collaborate with cross-functional teams to translate operational processes into agent capabilities, while deploying and maintaining infrastructure on Kubernetes using GitOps and observability practices.
Charger logistics Inc. is a world- class asset-based carrier with locations across North America. With over 20 years of experience providing the best logistics solutions, Charger logistics has transformed into a world-class transport provider and continue to grow.
We are looking for a highly motivated AI Engineer to join our team and contribute to the development of AI-driven solutions for various departments. This role focuses on building production AI agents and MCP (Model Context Protocol) integrations that automate real logistics workflows—dispatch, billing, compliance, and fleet operations—improving the reliability, transparency, and efficiency of AI applications in real-world, high-stakes environments.
Responsibilities:
- Design, develop, and deploy MCP servers exposing domain services as AI-consumable tools with proper authentication, observability, and error handling.
- Build multi-agent workflows using orchestration frameworks and agent-to-agent communication protocols for complex logistics automation.
- Develop and optimize knowledge retrieval pipelines using RAG, KAG, and CAG strategies—selecting the right approach based on query complexity, data volatility, and domain reasoning requirements.
- Design hybrid retrieval architectures that route between CAG for static reference data, RAG for dynamic operational queries, and KAG for multi-hop reasoning across structured domain knowledge.
- Implement LLM integration layers—prompt engineering, function calling, structured output parsing, and model routing for domain accuracy.
- Collaborate with cross-functional teams to collect requirements and translate operational workflows into agent capabilities.
- Deploy and maintain agent infrastructure on Kubernetes with GitOps practices and observability tooling.
Requirements
- Minimum 3 years of experience with Bachelor's in Computer Science, Artificial Intelligence, or a related technical field.
- Strong communication skills and experience working in interdisciplinary or team-based environments.
- Solid understanding of REST APIs, microservices architecture, and AI/ML concepts.
- Experience building production-grade AI applications in Python—not just notebooks or prototypes.
- Hands-on proficiency with LLM integration: function calling, tool use, structured outputs (OpenAI, Anthropic, or Google APIs).
- Solid understanding of knowledge retrieval patterns including RAG (Retrieval-Augmented Generation), with familiarity of emerging approaches like KAG (Knowledge-Augmented Generation) and CAG (Cache-Augmented Generation).
- Proficiency with SQL and at least one analytical data platform (BigQuery, Snowflake, or similar).
- Experience with cloud platforms and container orchestration (Kubernetes).
- Background in MCP, agent orchestration frameworks, knowledge graphs, or streaming data systems is a strong asset.
Benefits
- Competitive Salary
- Healthcare Benefit Package
- Career Growth
What you need to know about the Colorado Tech Scene
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
