8 hours ago
Remote, United States or San Antonio, TX, USASenior
Base Salary
$120k - $140k/yr
Responsibilities
- Lead the design, development, deployment, and support of agentic AI applications, including multi-agent workflows and orchestration.
- Provide hands-on technical direction, code review, support, and mentorship to AI engineers and contractors.
- Architect and implement RAG pipelines, vector search, embedding-based retrieval, and LLM integration patterns for production use cases.
- Design and build MCP Server integrations and tool-use frameworks.
- Translate AI strategy and roadmap into technical execution plans with engineering, data strategy, and cross-functional stakeholders.
- Select and integrate AI frameworks and platforms for scalable, reliable, and secure deployments.
- Establish engineering practices covering testing, responsible AI guardrails, and delivery automation.
- Monitor emerging AI engineering tools and techniques and assess their applicability.
Requirements
- Bachelor’s degree in a technical or numerical field is required; a master’s degree is preferred.
- At least 8 years of experience designing and developing software applications and/or data engineering pipelines.
- At least 3 years of experience leading or providing technical direction to engineers on application or AI/ML delivery teams.
- Required experience with LangGraph, LangChain, RAG chatbots, vector databases, NLP-based automation, MCP Server development, embedding-based search, and LLM integration.
- Required experience with microservices architecture, Kubernetes, OpenShift, AWS and/or Azure, Spring Boot, Node.js, CI/CD, and DevOps automation.
- Preferred experience with Trino, Superset, Starburst, SQL Server, ETL tools, S3, Airflow, Iceberg, PySpark, or Hive.
- Preferred experience building financial-services solutions in big-data infrastructure.
- Strong hands-on expertise in machine learning frameworks, agentic AI architecture, and cloud computing platforms.
- Working knowledge of AI ethics, data privacy, and regulatory considerations.
- Preferred certifications include AWS Certified Machine Learning Engineer, AWS Certified AI Practitioner, Microsoft Certified: Azure AI Engineer Associate, Google Cloud Professional Machine Learning Engineer, NVIDIA AI/Deep Learning certification, or Databricks Machine Learning Associate.
Benefits
- Medical, dental, and vision coverage.
- 401(k) matching and flexible PTO.
- Life insurance, employee assistance, and pet insurance.
- Equal-opportunity employment and disability accommodations are available.
Tech Stack
Apache AirflowApache HiveApache SupersetAWSAzureDatabricksGoogle CloudKubernetesMicrosoft SQL ServerNode.jsOpenShiftSpring Boot
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