Artificial Intelligence Engineer

About the Role

• Were looking for an experienced AI Engineer to help build the next generation of AI-powered
• products and workflows. This role is focused on applying the latest advances in generative AI—
• not training foundation models—to solve real business problems.
• Youll design, build, and deploy production-ready AI applications using large language models
• (LLMs), retrieval systems, agentic workflows, multimodal models, and modern AI development
• frameworks. Youll work closely with project managers, UX designers, software engineers, and
• business stakeholders to rapidly prototype ideas and evolve them into scalable solutions.
• This is an ideal role for someone who enjoys moving quickly, experimenting with new
• technologies, and translating emerging AI capabilities into practical business value.

What Youll Do

• • Design, develop, and deploy production-grade AI applications using modern LLMs and
• multimodal models.
• • Build Retrieval-Augmented Generation (RAG) systems leveraging embeddings, vector
• databases, hybrid search, and knowledge retrieval.
• • Develop intelligent agents capable of tool use, function calling, workflow orchestration,
• and autonomous task execution.
• • Integrate commercial and open-source foundation models including OpenAI, Anthropic,
• Google Gemini, and others.
• • Evaluate new models, frameworks, and prompting strategies to improve quality,
• reliability, latency, and cost.
• • Build APIs and backend services that expose AI capabilities to internal and external
• applications.
• • Develop evaluation pipelines and automated testing for prompts, agents, and AI
• workflows.
• • Implement observability, monitoring, guardrails, and feedback mechanisms for AI
• systems in production.
• • Partner with stakeholders to identify opportunities where AI can improve business
• processes and employee experiences.
• • Contribute to AI architecture decisions, coding standards, and engineering best
• practices.
• • Stay current with the rapidly evolving AI ecosystem and help drive technical innovation
• across the organization.

Required Qualifications

• 3–5 years of professional software engineering experience.
• 1–3 years building production applications utilizing Large Language Models or

Generative AI technologies.

• Strong proficiency in Python.
• Experience developing AI applications using frameworks such as LangChain,

• LangGraph, LlamaIndex, Semantic Kernel, DSPy, or similar.
• • Experience implementing RAG architectures and working with vector databases such as
• Pinecone, Weaviate, Milvus, Chroma, or pgvector.
• • Experience integrating LLM APIs including OpenAI, Anthropic, Google Gemini, or similar
• platforms.
• • Strong understanding of prompt engineering, structured outputs, tool calling, and
• function invocation.

• Experience building REST APIs and integrating AI capabilities into existing applications.
• Familiarity with cloud platforms (AWS, Google Cloud, or Azure).
• Experience with Docker, Git, CI/CD, automated testing, and modern software

• engineering practices.
• • Strong communication skills and the ability to explain technical concepts to both
• technical and non-technical audiences.

Preferred Qualifications

• Experience building autonomous or multi-agent systems.
• Experience using AI evaluation frameworks such as LangSmith, Arize Phoenix, Weights

• & Biases, Promptfoo, or DeepEval.
• • Experience with MCP (Model Context Protocol), A2A (Agent-to-Agent communication),
• or emerging AI interoperability standards.
• • Experience with multimodal AI including image, video, speech, or document
• understanding.
• • Experience deploying open-source models using vLLM, Ollama, Hugging Face, or
• similar inference platforms.

• Familiarity with Kubernetes and scalable cloud infrastructure.
• Background in MLOps, feature engineering, or traditional machine learning.
• Experience building internal AI products, developer tools, or workflow automation

platforms.

What Success Looks Like

Within your first year, youll have:

• Built and launched production AI applications used across the organization.
• Improved existing AI products through experimentation and evaluation.
• Established reusable patterns for agents, RAG systems, and prompt engineering.
• Helped define engineering standards for AI application development.
• Become a trusted technical partner for engineering and business teams.

Technologies We Use

• Python
• OpenAI, Anthropic, Google Gemini
• LangGraph, LangChain, LlamaIndex
• Vector databases (Pinecone, pgvector, Weaviate)
• FastAPI
• Docker
• Kubernetes
• GitHub Actions
• PostgreSQL
• Google Cloud Platform / AWS