Who You Are:

You are an AI-focused software engineer with a strong foundation in building production systems powered by large language models (LLMs), natural language processing (NLP), and intelligent automation. You thrive at the frontier of research and application, capable of turning cutting-edge models into secure, reliable, and scalable software. You’re comfortable in both Fortune 100–style environments and startup-style innovation, and you want to grow your career into senior AI leadership roles.

Key Responsibilities:

• Design, build, and deploy agentic AI systems that leverage LLMs, NLP, and retrieval-augmented generation (RAG) pipelines to support both client-facing and agent-facing use cases.

• Collaborate with data scientists, product managers, and engineers to translate ambiguous business problems into robust AI-driven solutions.

• Develop and maintain scalable APIs and microservices in Python and/or Java that integrate AI capabilities into enterprise applications.

• Implement best practices in MLOps, ensuring reproducibility, observability, and seamless deployment of AI models into production.

• Drive innovation by staying current with advancements in generative AI, multi-agent frameworks, and reinforcement learning.

• Contribute to the culture of security, compliance, and ethical AI adoption across the organization.

• Mentor junior engineers and contribute to the growth of Goosehead’s AI talent pipeline.

Required Qualifications

• 3–9 years of professional experience in software engineering or applied AI.

• Proficiency in Python and Java, with demonstrated experience building production-grade systems.

• Strong background in NLP, LLMs, and generative AI, including prompt engineering, fine-tuning, and orchestration frameworks.

• Hands-on experience with MLOps pipelines, containerization (Docker/Kubernetes), and CI/CD.

• Experience working with both structured and unstructured datasets.

• Strong problem-solving and communication skills, with the ability to explain technical concepts to non-technical stakeholders.

Preferred Qualifications

• Exposure to both Fortune 100 enterprises and startup environments, with the ability to balance rigor and speed.

• Experience with cloud platforms (Azure, AWS, or GCP) and vector databases.

• Familiarity with agentic AI frameworks, autonomous workflows, or multi-agent coordination systems.

• Background in insurance, financial services, or other regulated industries.

• Experience integrating AI into mission-critical, client-facing applications.