What does an AI Engineer do?

An AI Engineer builds product features powered by machine learning and large language models, then makes them reliable in production. That means designing RAG pipelines, fine-tuning or prompting models, writing evaluation harnesses, and managing the latency and cost of live inference. They sit between research and traditional software engineering.

What are the main responsibilities of an AI Engineer?

The core work is shipping model-backed features, building evaluations that catch regressions and hallucinations, and optimizing inference for cost and speed. AI Engineers also add guardrails, instrument observability for prompts and responses, and run experiments comparing new models against the current baseline.

What skills does an AI Engineer need?

Strong Python and solid software engineering fundamentals are essential, along with hands-on experience with LLM APIs, embeddings, and frameworks like PyTorch or LangChain. Just as important is a rigorous approach to evaluation, since shipping models responsibly depends on measuring quality rather than guessing.

What should an AI Engineer job description include?

A good AI Engineer job description opens with a clear definition and brief, then lists key responsibilities, full duties, and required experience. Naming your model stack, vector store, and evaluation approach helps candidates judge fit, since AI tooling varies widely between teams.

Who does an AI Engineer work with?

AI Engineers work closely with product managers, backend and platform engineers, and often data scientists or ML researchers. They also partner with design on how model output is surfaced to users and with security or legal teams on safety and data-handling questions.

Data & AI

AI Engineer Job Description

Data Analyst Data Scientist Machine Learning Engineer Data Engineer Business Analyst Data Architect Business Intelligence (BI) Analyst Database Administrator (DBA)MLOps Engineer

About the role

We are hiring an AI Engineer to take models from prototype to production and keep them dependable at scale. You will build retrieval pipelines, fine-tune and prompt models, wire up evaluation harnesses, and own the latency, cost, and quality of what ships. This role fits someone who is as comfortable reading a research paper as they are debugging a flaky inference endpoint at 2am.

Key responsibilities

Build and ship LLM and ML features into production systems
Design evaluation and guardrails to keep model output reliable
Optimize inference for latency, cost, and accuracy

Responsibilities

Design and build retrieval-augmented generation (RAG) pipelines with vector stores like Qdrant or Pinecone
Fine-tune, prompt-engineer, and adapt foundation models for specific product tasks
Build offline and online evaluation harnesses that measure accuracy, hallucination, and regressions
Serve models through scalable inference endpoints and manage token cost and latency budgets
Integrate model outputs behind APIs, streaming responses, and function or tool calling
Implement guardrails, content filtering, and fallback logic for unsafe or low-confidence output
Instrument tracing and observability for prompts, embeddings, and model responses
Curate, clean, and version training and evaluation datasets with clear provenance
Collaborate with product to translate fuzzy requirements into testable model behavior
Stay current with new models and techniques and run structured experiments to compare them

Requirements

Bachelor's degree in computer science, machine learning, or a related field, or equivalent hands-on experience
3 or more years building software, with at least 1 year shipping ML or LLM features to production
Strong Python skills and fluency with frameworks such as PyTorch, LangChain, or Hugging Face
Proven experience deploying models behind real APIs, not just notebooks or demos
Solid grasp of evaluation, prompt design, and the tradeoffs between accuracy, cost, and latency

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