Asks for

The ad says“Fluent daily use of AI coding agents (Claude Code, Cursor, GitHub Copilot or similar) as part of how you actually build, not as an occasional experiment.”
The ad says“Strong Python fundamentals — comfortable with async, type hints, testing, and packaging, and able to write code that another engineer can read and maintain.”
The ad says“Working knowledge of SQL and the data layer behind AI systems — relational databases and at least one vector store, including embeddings, chunking, and basic query tuning — together with REST API development, ideally in FastAPI.”
The ad says“Working knowledge of SQL and the data layer behind AI systems — relational databases and at least one vector store, including embeddings, chunking, and basic query tuning — together with REST API development, ideally in FastAPI.”
The ad says“Working knowledge of SQL and the data layer behind AI systems — relational databases and at least one vector store, including embeddings, chunking, and basic query tuning — together with REST API development, ideally in FastAPI.”
The ad says“Hands-on experience building LLM applications — LangGraph or a comparable orchestration framework (LangChain, LlamaIndex, or an agent SDK), plus prompt engineering and retrieval/RAG patterns, demonstrated through professional work, internships, or substantial personal projects.”
The ad says“Hands-on experience building LLM applications — LangGraph or a comparable orchestration framework (LangChain, LlamaIndex, or an agent SDK), plus prompt engineering and retrieval/RAG patterns, demonstrated through professional work, internships, or substantial personal projects.”
The ad says“Strong Python fundamentals — comfortable with async, type hints, testing, and packaging, and able to write code that another engineer can read and maintain.”
The ad says“Hands-on experience building LLM applications — LangGraph or a comparable orchestration framework (LangChain, LlamaIndex, or an agent SDK), plus prompt engineering and retrieval/RAG patterns, demonstrated through professional work, internships, or substantial personal projects.”
The ad says“Strong Python fundamentals — comfortable with async, type hints, testing, and packaging, and able to write code that another engineer can read and maintain.”
The ad says“Working knowledge of SQL and the data layer behind AI systems — relational databases and at least one vector store, including embeddings, chunking, and basic query tuning — together with REST API development, ideally in FastAPI.”
The ad says“Working knowledge of SQL and the data layer behind AI systems — relational databases and at least one vector store, including embeddings, chunking, and basic query tuning — together with REST API development, ideally in FastAPI.”
The ad says“Working knowledge of SQL and the data layer behind AI systems — relational databases and at least one vector store, including embeddings, chunking, and basic query tuning — together with REST API development, ideally in FastAPI.”
The ad says“Hands-on experience building LLM applications — LangGraph or a comparable orchestration framework (LangChain, LlamaIndex, or an agent SDK), plus prompt engineering and retrieval/RAG patterns, demonstrated through professional work, internships, or substantial personal projects.”
The ad says“Working knowledge of SQL and the data layer behind AI systems — relational databases and at least one vector store, including embeddings, chunking, and basic query tuning — together with REST API development, ideally in FastAPI.”
The ad says“Strong Python fundamentals — comfortable with async, type hints, testing, and packaging, and able to write code that another engineer can read and maintain.”
The ad says“Strong Python fundamentals — comfortable with async, type hints, testing, and packaging, and able to write code that another engineer can read and maintain.”
The ad says“Working knowledge of SQL and the data layer behind AI systems — relational databases and at least one vector store, including embeddings, chunking, and basic query tuning — together with REST API development, ideally in FastAPI.”

Values

The ad says“Exposure to agent protocols and multi-agent patterns — MCP servers and tooling, agent SDKs, orchestration and hand-off design, or human-in-the-loop workflows.”
The ad says“Front-end familiarity — React or Angular with TypeScript, enough to build a usable demo or internal tool without waiting on someone else.”
The ad says“Cloud and delivery basics on AWS — containerised deployment, serverless, CI/CD, and an eye on latency and token cost.”
The ad says“Cloud and delivery basics on AWS — containerised deployment, serverless, CI/CD, and an eye on latency and token cost.”
The ad says“Fluent daily use of AI coding agents (Claude Code, Cursor, GitHub Copilot or similar) as part of how you actually build, not as an occasional experiment.”
The ad says“Cloud and delivery basics on AWS — containerised deployment, serverless, CI/CD, and an eye on latency and token cost.”
The ad says“Fluent daily use of AI coding agents (Claude Code, Cursor, GitHub Copilot or similar) as part of how you actually build, not as an occasional experiment.”
The ad says“Experience with LLM evaluation and observability tooling — LangFuse, OpenTelemetry, Ragas, DeepEval, or an in-house equivalent.”
The ad says“Fluent daily use of AI coding agents (Claude Code, Cursor, GitHub Copilot or similar) as part of how you actually build, not as an occasional experiment.”
The ad says“Experience with LLM evaluation and observability tooling — LangFuse, OpenTelemetry, Ragas, DeepEval, or an in-house equivalent.”
The ad says“Experience with LLM evaluation and observability tooling — LangFuse, OpenTelemetry, Ragas, DeepEval, or an in-house equivalent.”
The ad says“Exposure to agent protocols and multi-agent patterns — MCP servers and tooling, agent SDKs, orchestration and hand-off design, or human-in-the-loop workflows.”
The ad says“Experience with LLM evaluation and observability tooling — LangFuse, OpenTelemetry, Ragas, DeepEval, or an in-house equivalent.”
The ad says“Experience with LLM evaluation and observability tooling — LangFuse, OpenTelemetry, Ragas, DeepEval, or an in-house equivalent.”
The ad says“Experience with LLM evaluation and observability tooling — LangFuse, OpenTelemetry, Ragas, DeepEval, or an in-house equivalent.”
The ad says“Front-end familiarity — React or Angular with TypeScript, enough to build a usable demo or internal tool without waiting on someone else.”
The ad says“Cloud and delivery basics on AWS — containerised deployment, serverless, CI/CD, and an eye on latency and token cost.”
The ad says“Front-end familiarity — React or Angular with TypeScript, enough to build a usable demo or internal tool without waiting on someone else.”

Why Mobility Global?

At Mobility Global, our mission is to provide the trusted information that helps billions of people build, sell, and own vehicles with confidence. As we shape the next generation of automotive intelligence through innovation, AI, and industry-leading data, we're creating new opportunities for our customers, our business, and our people.

Join a team singularly focused on transforming the automotive industry. S&P Global Mobility is Mobility Global — the company behind CARFAX, automotiveMastermind, Market Scan, and Polk Automotive Solutions. Our strength comes from our people, and here, the progress you make helps the entire automotive ecosystem move faster and with greater confidence.

The Team:

You will join the AI Engineering team at Mobility Global. We build the conversational analytics products, shared AI services, and agent platforms that sit on top of the industry’s deepest automotive dataset, and we build the internal tooling that drives AI adoption across the PDLC/SDLC, from requirements and design through code, testing, and release. We are a small, senior, product-minded team that owns its work end to end, from the React front end a customer touches through the FastAPI services and data layer behind it to the evaluation harnesses that tell us whether the answer was right.

We work AI-natively by default: coding agents, agentic workflows, and LLM-assisted review are part of how the team ships every day, not a side experiment, and we care as much about engineering judgement as we do about raw output. This role is a deliberate early-career seat on that team, with senior engineers close by and real production ownership from the first quarter.

Responsibilities and Impact:

Build LLM-powered features in Python — agentic workflows in LangGraph, retrieval and RAG pipelines, text-to-SQL, and tool/MCP integrations that reach real users rather than staying in a notebook.
Ship the services behind them — write clean, typed, tested Python and FastAPI endpoints, including streaming responses, async I/O, and sensible error handling.
Own evaluation and quality for what you build — assemble golden datasets, write eval and guardrail test suites, instrument traces, and use the results to decide whether a change actually helped.
Do prompt and context engineering as an engineering discipline — iterate on prompts, tool schemas, and retrieval strategy, and measure the difference instead of guessing at it.
Debug agent behaviour end to end — read traces, isolate failures across retrieval, tool calls, parsing, and entitlements, and turn each one into a regression test.
Work daily with AI coding agents and with the people around you — use agents to move faster while reviewing their output carefully, and pair with senior engineers, data scientists, and product partners to learn the domain quickly.

What We’re Looking For:

Basic Required Qualifications:

3-6 years of professional software engineering experience, with a Bachelor’s degree in Computer Science, Engineering, or a related field, or equivalent practical experience.
Strong Python fundamentals — comfortable with async, type hints, testing, and packaging, and able to write code that another engineer can read and maintain.
Hands-on experience building LLM applications — LangGraph or a comparable orchestration framework (LangChain, LlamaIndex, or an agent SDK), plus prompt engineering and retrieval/RAG patterns, demonstrated through professional work, internships, or substantial personal projects.
Working knowledge of SQL and the data layer behind AI systems — relational databases and at least one vector store, including embeddings, chunking, and basic query tuning — together with REST API development, ideally in FastAPI.
Fluent daily use of AI coding agents (Claude Code, Cursor, GitHub Copilot or similar) as part of how you actually build, not as an occasional experiment.
Clear written and verbal communication — can explain what you built, what you tried, and what did not work, to both technical and non-technical colleagues.
Genuine curiosity and speed of learning — keeps up with a field that changes monthly and brings what you find back to the team.
Ownership and healthy scepticism — follows a problem through to a verified fix, and checks AI-generated output rather than trusting it by default.
Collaborative and coachable — asks for help early, takes review feedback well, and works comfortably across time zones in a distributed team.

Additional Preferred Qualifications:

Experience with LLM evaluation and observability tooling — LangFuse, OpenTelemetry, Ragas, DeepEval, or an in-house equivalent.
Exposure to agent protocols and multi-agent patterns — MCP servers and tooling, agent SDKs, orchestration and hand-off design, or human-in-the-loop workflows.
Cloud and delivery basics on AWS — containerised deployment, serverless, CI/CD, and an eye on latency and token cost.
Front-end familiarity — React or Angular with TypeScript, enough to build a usable demo or internal tool without waiting on someone else.
Interest in the data foundations — data governance, semantic or ontology modelling, entitlements, or large-scale analytics products; automotive or mobility domain exposure is a plus.
Evidence of building things for their own sake — open-source contributions, side projects, technical writing, or community involvement.

This is a hybrid role with in-office expectations determined by location and subject to change based on evolving business needs.

What’s In It for You

Competitive compensation package, including base salary and incentive opportunities where applicable.
Comprehensive health and wellness benefits for employees and eligible dependents.
Retirement savings programs, including company matching contributions where available.

Equal Opportunity Employer

Mobility Global is proud to be an Equal Opportunity Employer. We value diverse perspectives and are committed to fostering an inclusive workplace where everyone is respected, supported, and empowered to succeed. We provide reasonable accommodations throughout the hiring process and employment for qualified individuals with disabilities.

Recruitment Fraud Alert

Mobility Global will never ask candidates to pay money at any stage of the hiring process, including for applications, interviews, background checks, training, or equipment. If you receive a suspicious communication or request for payment or personal financial information, please exercise caution and report it immediately.