Artificial Intelligence Engineer

Role Overview

As an AI Engineer, you will be responsible for the end-to-end development of agentic AI applications that move beyond simple chatbots into autonomous, goal-oriented systems. You will design reasoning loops, integrate specialized tools, and build the automated evaluation pipelines (AgentOps) necessary to ensure these systems are reliable, safe, and performant in production.

Key Responsibilities

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Agent Architecture: Design and implement multi-agent workflows using frameworks such as LangGraph, CrewAI, or AutoGen (AG2) to solve complex, multi-step business problems.

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Tool & API Integration: Build secure interfaces that allow agents to interact with external data environments (e.g., Snowflake, Vector DBs) and enterprise APIs.

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Cognitive Design: Implement sophisticated memory management (short-term state and long-term RAG) and reasoning strategies (ReAct, Reflection) to reduce hallucinations.

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AgentOps & CI/CD: Develop and maintain automated LLM-as-a-Judge evaluation suites within the CI/CD pipeline to gate deployments based on factuality, safety, and task completion.

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Observability: Set up advanced tracing and monitoring (e.g., LangSmith, Arize Phoenix) to debug agent thought processes and optimize token costs/latency.

Technical Skillset

Category

Required Proficiency

Languages

Python (Expert), Asynchronous programming, SQL.

AI Frameworks

LangChain/LangGraph, LlamaIndex, CrewAI, AutoGen. (at least two)

Foundational Models

Azure AI Foundry, OpenAI API, AWS Bedrock, or local LLM deployment (Ollama/vllm).

Infrastructure & Ops

Docker, GitHub Actions/GitLab CI, Vector Databases (Pinecone, Weaviate, Milvus).

Evaluation Tools

G-Eval, Ragas, DeepEval, or custom scoring rubrics.

Experience & Professional Profile

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End-to-End Ownership: Demonstrated experience taking AI features from initial concept through to deployment. Comfortable managing both the application logic and the basic infrastructure required to support it.

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Rapid Prototyping: Experience with frameworks like Streamlit or Gradio is highly desirable, as it shows a strong aptitude for the AI demo space

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Problem Decomposition: Ability to break down complex business requirements into clear, logical steps that an AI system can execute effectively.

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Focus on Reliability: Strong understanding of the limitations of Large Language Models. Prioritizes building fail-safes, validation checks, and error-handling routines to ensure consistent system performance.

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Collaborative Mindset: Experience working alongside Data Engineers and DevOps teams to ensure AI components are integrated seamlessly into the broader enterprise ecosystem.

The ideal candidate views an LLM as a component in a larger software machine, not the machine itself.