- Company Name
- Distyl
- Job Title
- AI Engineer
- Job Description
-
Job Title: AI Engineer
Role Summary: Design, develop, and deploy robust, production‑grade AI systems powered by large language models (LLMs) for enterprise clients. Lead architecture decisions, build end‑to‑end AI workflows, and collaborate closely with stakeholders to deliver scalable, cost‑efficient solutions that meet stringent reliability, observability, and security standards.
Expectations: • Deliver production-ready LLM applications, including prompt engineering, agent workflows, and tool integration.
• Partner with enterprise customers to translate complex operational challenges into AI solutions.
• Own system architecture from prototype through deployment, ensuring performance, safety, and maintainability.
• Contribute to internal platform development (Distillery) to create reusable infrastructure and workflows.
• Establish evaluation frameworks measuring accuracy, latency, cost, reliability, and safety.
• Promote engineering excellence by optimizing development methods, observability, and deployment practices.
Key Responsibilities:
- Build production AI systems using LLMs, including prompt engineering, RAG pipelines, and multi‑step workflows.
- Engage directly with customers to scope problems, define solution requirements, and validate outcomes.
- Design scalable, secure, and maintainable architectures for high‑volume enterprise workloads.
- Develop reusable components for the internal LLM application platform.
- Create and maintain evaluation metrics and testing harnesses for model performance and reliability.
- Ensure compliance with observability, security, and governance standards.
- Drive continuous improvement of engineering processes, tooling, and deployment strategies.
Required Skills:
- 3+ years professional software engineering experience.
- Proficiency in Python or TypeScript.
- Proven experience building and deploying LLM‑powered applications or agents at production scale.
- Familiarity with modern LLM frameworks (LangChain, LlamaIndex, Guardrails, MCP, etc.).
- Experience with RAG pipelines, tool integration, and multi‑step AI workflows.
- Strong grasp of AI system evaluation, debugging, observability, and performance tuning.
- Knowledge of DevOps practices (CI/CD, monitoring, observability, containerization).
- Experience with cloud platforms (AWS, GCP, Azure) is a plus.
- Understanding of responsible AI principles (auditability, governance) is a plus.
- Experience with agent architectures and long‑horizon task execution is a plus.
Required Education & Certifications:
- Bachelor’s degree in Computer Science, Software Engineering, or a closely related field.
- Relevant certifications (e.g., AWS Certified Machine Learning, Google Professional ML Engineer) are advantageous but not mandatory.