AI Engineer

The Role

We are seeking an AI Engineer to design, build, test, and improve AI-powered applications across our marketing agency and related digital platforms.

You will work on systems that use large language models, structured data, retrieval, automation, and external APIs to perform useful work—not merely generate text. Projects may include internal knowledge assistants, research and content systems, conversational interfaces, intelligent workflows, evaluation pipelines, and client-facing AI features.

This is an applied engineering role. We need someone who can select the right models and architecture, develop working systems around them, evaluate their performance, and make them dependable enough for real-world use.

You should understand that a successful AI prototype is only the beginning. Your work must address retrieval quality, hallucination risk, data structure, observability, security, latency, cost, failure handling, and maintainability.

What You’ll Work On

• Build AI-powered applications, assistants, agents, and workflow automations
• Develop retrieval-augmented generation systems using structured and unstructured company knowledge
• Connect language models with databases, APIs, search systems, business software, and internal tools
• Design prompts, tool-calling logic, context-management systems, and multi-step AI workflows
• Create data-ingestion, cleaning, chunking, indexing, metadata, and retrieval pipelines
• Develop evaluation systems that measure accuracy, relevance, consistency, completeness, and failure rates
• Compare models, retrieval methods, frameworks, and architectural approaches
• Reduce hallucinations through grounding, validation, citations, structured outputs, and appropriate human review
• Build safeguards, fallbacks, monitoring, logging, and error-handling systems
• Optimize applications for response quality, latency, token consumption, and operating cost
• Create APIs and backend services that expose AI capabilities to websites and internal applications
• Document system architecture, data flows, prompts, dependencies, limitations, and deployment procedures
• Work with developers, researchers, marketers, and operational teams to translate business needs into technical systems
• Improve existing prototypes and automations so they become reliable, maintainable production tools

You will be encouraged to:

• Challenge proposed AI features when a conventional software solution would be more reliable or economical
• Begin with the simplest architecture capable of solving the problem
• Test assumptions with measurable evaluations rather than impressive demonstrations
• Identify failure modes before systems reach users
• Build reusable components instead of isolated experiments
• Remain current with rapidly changing models, tools, and engineering practices

Tools & Technical Mastery

Strong candidates demonstrate proficiency in:

• Python and production-quality backend development
• LLM APIs such as OpenAI, Anthropic, Google Gemini, or comparable platforms
• Retrieval-augmented generation, embeddings, semantic search, reranking, and hybrid retrieval
• Vector databases or search systems such as Pinecone, Weaviate, Qdrant, Elasticsearch, pgvector, or comparable technologies
• Relational databases, preferably PostgreSQL, and practical data modeling
• API development using frameworks such as FastAPI, Flask, Django, Node.js, or comparable technologies
• Structured outputs, function calling, tool use, and agentic workflows
• Prompt engineering, context engineering, and prompt-version management
• Automated and human evaluation methods for AI-generated outputs
• Git, testing, debugging, code review, deployment, and technical documentation
• Cloud infrastructure and containerized deployment using services such as AWS, Google Cloud, Azure, Docker, or comparable platforms

Experience with the following is valuable:

• LangChain, LlamaIndex, DSPy, LangGraph, or similar frameworks
• Knowledge graphs, entity resolution, ontologies, or structured knowledge systems
• Model Context Protocol and interoperable AI tools
• Fine-tuning, distillation, classification, or traditional machine-learning methods
• AI observability and evaluation platforms
• Web scraping, document processing, OCR, and data-enrichment pipelines
• Workflow automation platforms such as n8n, Make, or Zapier
• Authentication, permissions, data privacy, and multi-tenant application design
• JavaScript or TypeScript and modern front-end frameworks

We care more about sound engineering judgment than familiarity with any particular framework. AI tools change quickly; your ability to evaluate them matters more than collecting tool names.

Who This Role Is For

Strong candidates typically:

• Have 3+ years of software, data, machine-learning, or AI engineering experience
• Have built AI applications that progressed beyond demonstrations or notebooks
• Can show systems they personally designed, developed, tested, or deployed
• Understand the difference between prompt experimentation and production AI engineering
• Can determine when to use retrieval, structured data, conventional code, workflow automation, or model reasoning
• Understand databases, APIs, backend services, authentication, and deployment
• Can diagnose whether poor performance originates in the model, prompt, retrieval system, source data, architecture, or product requirements
• Evaluate outputs systematically rather than relying on anecdotal examples
• Write clear, maintainable, documented code
• Balance quality, development speed, latency, operating cost, and technical complexity
• Follow developments in AI without adopting every new framework or technique uncritically
• Communicate technical concepts clearly to non-technical colleagues
• Work independently while collaborating effectively with developers and subject-matter experts
• Communicate clearly and professionally in English

A degree in computer science, software engineering, data science, machine learning, or a related field is valuable, but demonstrated technical ability and completed work are more important.

What You’ll Gain

• The opportunity to build AI systems that become part of real business operations and customer-facing products
• Ownership of AI architecture, evaluation, implementation, and continuous improvement
• Exposure to substantial proprietary knowledge, content, research, marketing, and business data
• The freedom to test new models and approaches when there is a credible business case
• Direct collaboration with company leadership, engineers, researchers, and operational teams
• Performance-based financial upside tied to agreed technical and business outcomes
• A high-trust environment focused on sound judgment, accountability, and continuous improvement
• Support for technical experimentation, AI tools, and professional development
• Flexibility around genuine personal and family needs, with disciplined working hours that protect evenings and weekends.

How We Evaluate Fit

We care less about formal titles and more about:

• The AI applications and systems you have personally built
• What reached production and how it was used
• Your specific contribution to each project
• How you selected models, tools, retrieval methods, and architecture
• How you evaluated accuracy and identified failure modes
• How you handled hallucinations, unreliable retrieval, incomplete data, and edge cases
• Your ability to distinguish a compelling demonstration from a dependable product
• The quality, clarity, and maintainability of your code
• Your understanding of APIs, databases, deployment, monitoring, and security
• Your ability to explain architectural decisions and tradeoffs
• Evidence that your work created measurable operational or commercial value
• Your judgment about when AI should—and should not—be used

The process includes:

• Résumé or LinkedIn profile review
• A short video introduction
• A technical conversation about systems you have personally built
• Review of relevant code, architecture diagrams, repositories, demonstrations, or case studies
• A paid practical assignment involving a realistic AI-engineering problem

Make sure to include:

• Coverletter
• Video intro link (see below)
• Resume/CV
• Supporting materials (portfolio, writing examples, etc.), if applicable

---

Note:

• We only guarantee responses to applicants that have included video intros.

---

We look forward to welcoming the right people onto our awesome team!

VIDEO INTRO

So you reviewed our site, including our About Us and Why DMS Advantage pages, and are interested in working with us? Great!

We want to get a more personal understanding of you. To help us do so, please record a 5-minute video introduction of yourself using a free app such as Vidyard or YouTube unlisted sharing (which is private) or add it to your Google Drive and share the link.

Send a streaming link—no downloadable files.

Please do NOT read a script.

We want to hear you discuss yourself in your own voice.

In the video, please answer the following questions:

1. Give us an overview of your professional background and share your greatest professional achievements: What do you believe you can bring to the table and what would you most enjoy doing?
2. Explain the challenges of your job and how you approach them. Take a moment to discuss the specifics so we can better understand your working style and capabilities.
3. Share how you use AI as part of your workflow. Discuss your interest in AI.
4. Discuss your availability for work: What are your preferred work hours, and do you have any other personal or professional responsibilities that may impact your availability?
5. If applicable, explain why you are leaving your current job or why you stopped working at your last position.
6. Share your current salary or most recent wage and what your salary expectations are.
7. Looking ahead, where do you see yourself professionally in five and ten years from now?