AI Engineer III

Vericast•San Antonio, TX

12d•$120,000 - $140,000•Onsite

About The Position

The AI Engineer III is a senior hands-on technical role responsible for the hands-on development, delivery and support of Agentic AI applications across the enterprise. This individual will serve as the technical lead for a team of AI Engineers, setting engineering standards, collaborating in architecting agentic solutions, and ensuring AI systems are built reliably, securely, and in alignment with business objectives. The role partners closely with the VP Engineering and Data Strategy on execution strategy while owning day-to-day technical delivery and support.

Requirements

Bachelor's Degree in a technical or numerical field (Required). Master's degree preferred.
8+ years' experience designing and developing software applications and/or data engineering pipelines
3+ years' experience leading or providing technical directions to engineers on application or AI/ML delivery teams
Experience in LangGraph, LangChain, RAG chatbots, vector databases, NLP-based automation, MCP Server development, embedding-based search, and LLM integration is required
Experience in microservices architecture, Kubernetes, OpenShift, AWS and/or Azure, Spring Boot, Node.js, CI/CD, and DevOps automation is required

Nice To Haves

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 preferred
AWS Certified Machine Learning Engineer - Associate (MLA-C01) or AWS Certified AI Practitioner
Microsoft Certified: Azure AI Engineer Associate (AI-102)
Google Cloud Professional Machine Learning Engineer
NVIDIA AI/Deep Learning certification (e.g., NVIDIA Certified Associate: AI Infrastructure)
Databricks Machine Learning Associate

Responsibilities

Serve as hands-on technical lead for the design, development, deployment and support of Agentic AI applications, including multi-agent workflows and orchestration (25%)
Hands-on technical coloration, code review, support and mentorship to AI Engineers and contractors on the team (15%)
Architect and implement RAG pipelines, vector search, embedding-based retrieval, and LLM integration patterns for production use cases (15%)
Design and build MCP Server integrations and tool-use frameworks to extend agentic application capabilities (10%)
Collaborate with the VP Engineering and Data Strategy and cross-functional stakeholders to translate AI strategy and roadmap into technical execution plans (10%)
Own selection and integration of AI frameworks and platforms, ensuring scalability, reliability, and security of deployed solutions (10%)
Establish and enforce engineering best practices, including CI/CD, testing, and responsible AI guardrails for agentic systems (10%)
Monitor emerging AI engineering trends, tools, and techniques and assess applicability to current initiatives (5%)