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About This Role
Overview
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At Ford, you’ll work on ideas that matter, alongside passionate people who want to make a global impact. Together, we’re shaping the next era of transportation—grounded in purpose, driven by progress. Make your move.
- Job Type: Full time
- Work Type: Hybrid
We are seeking an accomplished, hands\-on Senior Software Engineer to lead the design and implementation of core artificial intelligence capabilities within our Intelligent Data Analytics Platform, with a particular emphasis on multi\-agent orchestration and semantic search. This position is intended for a highly capable individual contributor who is able to operate effectively at both architectural and implementation levels — an engineer who anchors the team technically by producing production\-grade code, resolving the most demanding problems, and establishing engineering standards by example.
The successful candidate will serve as a principal contributor to an AI\-first platform that enables users to explore, query, and analyze enterprise BigQuery data through agentic tools and capabilities.
1\. Architecture and System Design
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Contribute to the design of scalable, multi\-agent AI architectures.
Design components and modules across agent orchestration, tool systems, and large language model (LLM) integration.
Evaluate trade\-offs across architectural choices (e.g., single\- versus multi\-agent designs, retrieval\-augmented generation versus fine\-tuning, deterministic versus probabilistic pipelines).
Participate in design reviews and contribute to Architecture Decision Records (ADRs).
2\. Hands\-On Engineering and Execution
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Produce production\-grade code across agent frameworks, backend APIs, and frontend interfaces on a daily basis.
Develop and evolve reusable AI components, including agent tools, embedding pipelines, and evaluation frameworks.
Implement LLM\-powered workflows, including natural\-language\-to\-SQL generation, semantic search, and metadata enrichment.
Develop services that enable intelligent data access, such as vector search, hybrid retrieval, and query scope management.
Implement guardrails, validation layers, and observability mechanisms for AI\-generated outputs.
3\. Full\-Stack Development
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Build performant backend services (Python/ FastAPI) and interactive frontend applications (Angular/React) for data exploration.
Develop both conversational (chat) and structured (API) interfaces for analytical workloads.
Construct evaluation and benchmarking tooling to support continuous measurement of AI quality.
Assume end\-to\-end ownership of features, from initial design through deployment and ongoing monitoring.
4\. Semantic Search and Embeddings
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Implement vector embedding pipelines for metadata discovery using pgvector.
Develop semantic retrieval capabilities across datasets, tables, and columns, employing hybrid search strategies.
Optimize search relevance through embedding strategies, re\-ranking, and rigorous evaluation metrics.
Contribute to the platform's data quality and governance capabilities.
5\. Engineering Excellence
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Produce clean, maintainable, and scalable code that adheres to industry best practices.
Participate actively in code reviews and establish quality standards through exemplary personal contributions.
Conduct root\-cause analysis on agent failures and implement systematic remediations.
Serve as the team's technical anchor and primary point of reference for complex implementation challenges.
6\. Collaboration
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Partner with Product, Data Engineering, and Platform teams to ensure successful feature delivery.
Support colleagues through pair programming, knowledge sharing, and technical mentorship.
Contribute to sprint planning, effort estimation, and technical feasibility assessments.
Assist in onboarding new team members and disseminating domain expertise across the organization.
Required Qualifications
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Bachelor’s degree in Computer Science, Engineering, Data Science, or a related technical field.
8 \+ years of professional software engineering experience with demonstrated hands\-on coding proficiency.
Demonstrable experience building AI\-powered applications or operating LLM\-based systems in production environments.
Proven ability to interpret ambiguous requirements and independently deliver functional, well\-tested software.
Strong debugging and problem\-solving capabilities across the full technology stack.
A demonstrated record of owning and delivering complex features from inception through completion.
Technology Stack
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Programming Languages and Frameworks: Python (primary), Java, JavaScript/TypeScript, Angular/React
Artificial Intelligence and Machine Learning: Google ADK, LangChain/LangGraph, OpenAI and Gemini APIs, prompt engineering, retrieval augmented generation (RAG) pipelines
Data and Cloud Infrastructure: Google Cloud Platform (BigQuery, Vertex AI, and Cloud Run preferred)
Backend Technologies: FastAPI, Pydantic, SQLModel/SQLAlchemy, PostgreSQL with pgvector
Frontend Technologies: Angular or React, TypeScript
Continuous Integration, Continuous Delivery, and Infrastructure: Terraform, GitHub Actions, Docker Evaluation: Custom evaluation frameworks, LLM\-as\-judge methodologies
Preferred Qualifications
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Experience with the Google Agent Development Kit (ADK) or comparable agent frameworks, such as CrewAI, or LangGraph.
Applied machine learning experience encompassing embeddings, classification, clustering, natural language processing, and evaluation metrics.
Demonstrated experience with vector databases and semantic retrieval optimization.
Familiarity with data engineering practices and data governance processes.
Prior experience developing internal developer tooling or platform SDKs.
Role Details
About This Role
AI Software Engineers build the applications and systems that AI models run inside. They own the API layers, data pipelines, frontend integrations, and infrastructure that turn a model into a product users interact with. Every AI company needs engineers who can build the software around the AI.
The challenge is building reliable systems around inherently unreliable components. Models are probabilistic. They'll give different answers to the same question. They hallucinate. They're slow. They're expensive. Your job is to build an application layer that handles all of this gracefully while delivering a product that users trust and enjoy.
Across the 3,824 AI roles we're tracking, AI Software Engineer positions make up 7% of the market. At Ford Motor Company, this role fits into their broader AI and engineering organization.
AI Software Engineer roles are among the most numerous in the AI job market. Every company deploying AI needs software engineers who understand AI integration patterns. The demand is broad, spanning startups to enterprises, across every industry adopting AI capabilities.
What the Work Looks Like
A typical week includes: building API endpoints that serve model inference with caching and fallback logic, designing the data pipeline that feeds context to a RAG system, implementing streaming responses in the frontend, debugging a race condition in the async inference pipeline, and optimizing database queries for the vector search layer. It's full-stack engineering with AI at the center.
AI Software Engineer roles are among the most numerous in the AI job market. Every company deploying AI needs software engineers who understand AI integration patterns. The demand is broad, spanning startups to enterprises, across every industry adopting AI capabilities.
Skills Required
Full-stack engineering skills with AI integration experience. Python and TypeScript are the most common requirements. You'll need to understand API design, database architecture, and how to build reliable systems around probabilistic outputs. Experience with streaming, async processing, and caching patterns is increasingly important as real-time AI applications proliferate.
Knowledge of vector databases, embedding APIs, and LLM integration patterns (function calling, structured outputs, retry logic) differentiates AI software engineers from general software engineers. Understanding cost optimization (caching strategies, model routing, batched inference) is valuable since inference costs can dominate application economics.
Strong postings describe the product you'll be building, the AI integration patterns you'll work with, and the scale requirements. Look for companies that have existing AI features and need engineers to improve and expand them, not companies that are 'planning to add AI' someday.
Compensation Benchmarks
AI Software Engineer roles pay a median of $234,620 based on 682 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400.
Across all AI roles, the market median is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Safety ($274,200). By seniority level: Entry: $97,380; Mid: $160,000; Senior: $227,400; Director: $243,000; VP: $250,000.
Ford Motor Company AI Hiring
Ford Motor Company has 4 open AI roles right now. They're hiring across Data Scientist, AI Software Engineer, AI/ML Engineer. Positions span Remote, US, Dearborn, MI, US. Compensation range: $192K - $250K.
Location Context
Across all AI roles, 16% (613 positions) offer remote work, while 3,187 require on-site attendance. Top AI hiring metros: New York (2,448 roles, $210,000 median); San Francisco (1,990 roles, $253,000 median); Los Angeles (1,686 roles, $189,000 median).
Career Path
Common paths into AI Software Engineer roles include Software Engineer, Full-Stack Developer, Backend Engineer.
From here, career progression typically leads toward Staff Engineer, AI Architect, Engineering Manager.
If you're a software engineer, you're already 80% there. Learn the AI integration patterns: RAG, streaming inference, function calling, structured outputs. Build a project that demonstrates you can wrap an AI model in a production-quality application with proper error handling, caching, and user experience. That's the portfolio piece that gets you hired.
What to Expect in Interviews
Technical screens look like standard software engineering interviews with an AI twist. Expect system design questions about building reliable applications around probabilistic models: handling streaming responses, implementing retry logic for API failures, and designing caching strategies for LLM outputs. Coding rounds test standard algorithms plus practical integration patterns like async processing and rate limiting.
When evaluating opportunities: Strong postings describe the product you'll be building, the AI integration patterns you'll work with, and the scale requirements. Look for companies that have existing AI features and need engineers to improve and expand them, not companies that are 'planning to add AI' someday.
AI Hiring Overview
The AI job market has 3,824 open positions tracked in our dataset. By seniority: 119 entry-level, 1,813 mid-level, 1,472 senior, and 420 leadership roles (Director, VP, C-Level). Remote roles make up 16% of the market (613 positions). The remaining 3,187 roles require on-site or hybrid attendance.
The market median for AI roles is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($293,500 median, 31 roles); AI Safety ($274,200 median, 51 roles); Research Engineer ($260,000 median, 401 roles).
AI Software Engineer roles are among the most numerous in the AI job market. Every company deploying AI needs software engineers who understand AI integration patterns. The demand is broad, spanning startups to enterprises, across every industry adopting AI capabilities.
The AI Job Market Today
The AI job market spans 3,824 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,702), Data Scientist (281), AI Software Engineer (258). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (119) are outnumbered by mid-level (1,813) and senior (1,472) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 420 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 16% of all AI roles (613 positions), with 3,187 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $200,000. Top-quartile roles start at $253,000, and the 90th percentile reaches $307,500. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Engineering Manager roles lead at $293,500 median, while Prompt Engineer roles sit at $142,800. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,968 postings), Aws (1,203 postings), Azure (882 postings), Rag (877 postings), Gcp (735 postings), Prompt Engineering (587 postings), Pytorch (586 postings), Claude (554 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
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