AI Engineer

What You Will Do

Design, build, and support AI, data, and software solutions including data pipelines, APIs, applications, automations, and AI-enabled workflows.
Lead technical delivery across multiple systems while ensuring solutions are secure, scalable, supportable, and aligned with business objectives.
Apply AI-enabled development practices to accelerate delivery while maintaining engineering quality and operational excellence.
Partner with architects, business stakeholders, subject matter experts, and product teams to translate business needs into practical technical solutions.
Design and operationalize generative AI, agentic AI, semantic layers, and retrieval-augmented generation (RAG) capabilities.
Evaluate technical tradeoffs and make sound engineering decisions that balance delivery speed, risk, maintainability, and long-term value.
Provide technical leadership through architecture discussions, code reviews, design reviews, and mentoring of other engineers.
Continuously explore emerging technologies, AI models, frameworks, and engineering practices to improve solution outcomes and business impact.

Who You Are (Basic Qualifications)

Bachelor's degree in Computer Science, Engineering, Information Technology, or a related field, or equivalent practical experience.
5+ years of professional software engineering experience.
At least 1 year of hands-on experience developing AI-enabled or Generative AI solutions.
Experience designing, developing, deploying, and supporting production-grade software, data, and AI solutions.
Experience leading technical design or implementation efforts across complex, cross-functional initiatives.
Strong programming experience using Python and SQL in production environments.
Experience with source control, automated testing, CI/CD pipelines, observability, and modern software engineering practices.
Experience building cloud-native solutions on AWS, including serverless, event-driven, and containerized architectures.
Ability to communicate complex technical concepts and architectural decisions to both technical and non-technical stakeholders.
Experience mentoring and coaching engineers through technical guidance and collaborative development practices.

What Will Put You Ahead

Experience building AI-enabled solutions using large language models (LLMs), embeddings, graph databases, graph query languages, semantic technologies, and RAG architectures.
Experience with MCP or similar agent integration standards.
Experience designing verification and evaluation frameworks for AI-generated outputs, including testing, monitoring, observability, and governance controls.
Experience designing distributed systems using microservices, event-driven architecture, API-first design, and modular architecture patterns.
Experience applying DevSecOps and production engineering practices, including infrastructure as code, automated security controls, secrets management, feature flags, and progressive deployment strategies.
Experience with enterprise data and AI platforms such as Microsoft Fabric, Fabric IQ, Snowflake, Azure AI Foundry, or Databricks.
Experience designing and deploying agentic AI solutions for conversational or action-oriented use cases.
Experience working with reusable platforms, enterprise-scale data products, and AI-enabled business solutions.
Master's degree in Computer Science, Engineering, or a related technical discipline.