AI Engineer III

Vericast•San Antonio, TX

About The Position

The AI Engineer III is a senior, hands-on technical role responsible for developing, delivering, and supporting Agentic AI applications across the enterprise. The position serves as technical lead for a team of AI Engineers, setting engineering standards, contributing to agentic solution architecture, and ensuring AI systems are reliable, secure, and aligned with business objectives. The role partners with the VP Engineering and Data Strategy on execution strategy while owning day-to-day technical delivery and support. The role includes designing and implementing multi-agent workflows and orchestration, RAG pipelines, vector search, embedding-based retrieval, LLM integrations, MCP Server integrations, and tool-use frameworks. It also involves mentoring engineers and contractors, selecting and integrating AI frameworks and platforms, and establishing engineering practices such as CI/CD, testing, and responsible AI guardrails. The position requires a bachelor's degree in a technical or numerical field and at least eight years of software application development and/or data engineering experience, including at least three years leading or providing technical direction to engineers. A master's degree and specified data-platform experience, financial-services solution experience, and industry certifications are preferred. Salary range is $120,000–$140,000 annually. Vericast offers medical, dental, and vision coverage, 401(k) matching, flexible PTO, life insurance, employee assistance, and pet insurance.

Requirements

Bachelor's degree in a technical or numerical field.
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.
Experience with LangGraph, LangChain, RAG chatbots, vector databases, NLP-based automation, MCP Server development, embedding-based search, and LLM integration.
Experience with microservices architecture, Kubernetes, OpenShift, AWS and/or Azure, Spring Boot, Node.js, CI/CD, and DevOps automation.
Hands-on expertise in machine learning frameworks, agentic AI architecture, and cloud computing platforms.
Ability to lead technical delivery and mentor engineers in a fast-paced environment.
Strong communication and facilitation skills, including the ability to translate business requirements into technical designs.
Working understanding of AI ethics, data privacy, and regulatory considerations relevant to AI systems.
Support and comply with company processes, audits, and safety and security policies.

Nice To Haves

Master's degree.
Experience with Trino, Superset, Starburst, SQL Server, ETL tools, S3, Airflow, Iceberg, PySpark, or Hive.
Experience building financial services solutions in big data infrastructure.
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 certification.
NVIDIA AI/Deep Learning certification, such as NVIDIA Certified Associate: AI Infrastructure.
Databricks Machine Learning Associate certification.

Responsibilities

Lead hands-on design, development, deployment, and support of Agentic AI applications, including multi-agent workflows and orchestration.
Provide technical collaboration, 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 to extend agentic application capabilities.
Work with the VP Engineering and Data Strategy and cross-functional stakeholders to translate AI strategy and roadmaps into technical execution plans.
Own selection and integration of AI frameworks and platforms, ensuring deployed solutions are scalable, reliable, and secure.
Establish and enforce engineering best practices, including CI/CD, testing, and responsible AI guardrails for agentic systems.
Monitor emerging AI engineering trends, tools, and techniques and assess their applicability to current initiatives.
Demonstrate company Core Values and use Performance Excellence principles to continuously improve effectiveness, efficiency, products, and services.
Support and comply with internal and external audits, including providing information, performing assigned compliance tasks, and preparing and maintaining evidence for internal controls.
Support and comply with safety and security policies to promote a healthy working environment.