Artificial Intelligence Engineer

Coda Search│Staffing; Town of Texas (WI); Hybrid; USD 160,000 - 210,000; Full time

Benefits offered by this job

Hybrid/Remote work option

Job summary

Coda Search│Staffing is partnering with an established, technology-forward organization investing heavily in AI-driven transformation. The AI Strategist / Architect will be the initial technical leader, collaborating with executive leadership to assess processes, identify high-value AI opportunities, and build prototypes and proofs-of-concepts demonstrating potential value.

This is a strategy plus hands-on execution role; you will architect, prototype, and build, guiding an expanding AI

Qualifications

• 8+ years across software engineering, architecture, data, ML or related disciplines.
• Hands-on experience designing and building AI/ML or Generative AI solutions.
• Strong Python development skills to independently build prototypes and MVPs.
• Experience with LLMs, Generative AI, RAG, vector retrieval, and modern AI architectures.
• Understanding of agentic AI and AI orchestration patterns.
• Experience evaluating commercial, open-source, public, and private model options.
• Experience with AWS, Azure, GCP, or comparable cloud AI environments.
• Strong understanding of data architecture and data quality impacts on AI systems.
• Experience considering AI security, governance, privacy, validation, and observability.
• Ability to translate ambiguous business problems into technical solutions.
• Strong executive communication skills and ability to explain AI concepts to non-technical stakeholders.

Responsibilities

• Define the AI Transformation Strategy with executive and business leaders.
• Evaluate opportunities across knowledge-intensive workflows and identify AI-enabled improvements.
• Build an AI transformation roadmap and prioritize initiatives.
• Architect production-ready AI systems spanning data ingestion, retrieval, and APIs.
• For prototypes, develop MVPs using Python, LLM APIs, RAG frameworks, and cloud services.
• Move from experimentation to scalable enterprise solutions with governance patterns.

Job description

We are partnering with an established, technology-forward organization that is making a significant investment in how Artificial Intelligence can transform its business. This is not a role focused on simply adding AI features to existing applications or deploying another off-the-shelf chatbot. Leadership wants to understand where AI can fundamentally change how work gets done, what technologies should be used, and how those opportunities can be turned into working solutions.

The AI Strategist / Architect will be the initial technical leader behind that transformation.

You will work directly with executive leadership to evaluate business processes, identify high-value AI opportunities, recommend the right approach, and then build the prototypes and proof-of-concepts that demonstrate what is possible.

This is intentionally a strategy + hands-on execution role. There is not a large AI engineering team waiting for you to hand requirements to. Initially, you will be expected to architect, experiment, prototype, and build. As the AI strategy matures and successful solutions are identified, the expectation is that an AI capability and team will grow around this role.

What You'll Do

Define the AI Transformation Strategy

• Partner directly with executive and business leaders to understand how the organization operates and identify processes where AI can create meaningful efficiency, automation, quality, or cost improvements.
• Evaluate opportunities across knowledge-intensive and highly manual workflows and determine where AI can augment employees, automate portions of a process, or fundamentally redesign how work is performed.
• Build and maintain an AI transformation roadmap that prioritizes initiatives based on business impact, feasibility, cost, risk, and time to value.
• Help leadership distinguish between what is technically interesting and what will actually create measurable business value.

Determine the Right AI Approach

For each business problem, determine the appropriate technical solution rather than forcing every problem into the same AI architecture.

• Evaluate and make recommendations across technologies and approaches including:
• Agentic AI and autonomous/semi-autonomous agents
• Large Language Models and Generative AI
• Retrieval-Augmented Generation (RAG)
• Public vs. private LLMs
• Vector search and knowledge retrieval
• Traditional machine learning where appropriate
• AI-enabled workflow automation
• Cloud-hosted vs. private AI infrastructure

Make thoughtful architecture decisions around accuracy, latency, security, scalability, cost, maintainability, and protection of proprietary intellectual property.

• This is not a pure strategy or advisory position.
• You will personally build early-stage AI solutions, prototypes, and proof-of-concepts to validate ideas before the organization makes larger investments.
• Develop working MVPs using technologies such as Python, LLM APIs, RAG frameworks, vector databases, agent frameworks, and cloud AI services.
• Move quickly from:
• Use rapid experimentation to answer questions such as: Can this actually work? How accurate can we make it? What does it cost? How much human involvement is still required? And does it create enough value to justify scaling?

Architect for Production

• For successful prototypes, define the architecture required to move from experimentation into scalable enterprise solutions.
• Design end-to-end AI systems spanning data ingestion, retrieval, model selection, orchestration, APIs, security, monitoring, and human interaction.
• Establish patterns for model evaluation, observability, auditability, governance, and human-in-the-loop validation.
• Determine how proprietary company knowledge and historical data can safely be incorporated into AI systems while protecting sensitive information and intellectual property.
• Serve as the organization's internal AI expert and trusted advisor.
• Translate rapidly evolving AI technologies into practical business implications and recommendations.
• Be equally comfortable discussing AI transformation strategy with a CEO or CIO and opening your laptop afterward to build the prototype that proves the concept.
• Provide leadership with clear recommendations around what the organization should pursue, should wait on, and should avoid.

What We’re Looking For

We are looking for someone who combines the mindset of an AI strategist, architect, and builder.

You should have strong experience designing AI solutions, but you also need the curiosity and technical ability to personally experiment with new technologies and determine how they can solve real business problems.

Required Experience

• 8+ years of experience across software engineering, architecture, data, machine learning, or related technology disciplines
• Meaningful hands-on experience designing and building AI/ML or Generative AI solutions
• Strong Python development skills with the ability to independently build prototypes and MVPs
• Experience with LLMs, Generative AI, RAG, vector retrieval, and modern AI architectures
• Understanding of agentic AI and AI orchestration patterns
• Experience evaluating commercial, open-source, public, and private model options
• Experience with AWS, Azure, GCP, or comparable cloud AI environments
• Strong understanding of data architecture and how data quality impacts AI systems
• Experience considering AI security, governance, privacy, validation, and observability
• Ability to translate ambiguous business problems into technical solutions
• Strong executive communication skills and the ability to explain complex AI concepts to non-technical stakeholders

The Person Who Will Thrive Here

• You aren't someone who needs a fully defined AI roadmap handed to you.
• You're the person who creates it.
• You enjoy walking into an organization, learning how the business actually operates, finding the areas where AI could materially change the economics or efficiency of a process, and then figuring out whether the idea really works.
• You aren't tied to one technology or framework. Sometimes the answer may be an agent. Sometimes it may be RAG. Sometimes an existing commercial solution may be the smartest answer. And sometimes the correct recommendation may be not to use AI at all.
• Most importantly, you're comfortable operating between the whiteboard and the keyboard. You can help executives decide where the organization should go with AI, but you can also build the first version that gets them there.

This role is Hybrid/Remote with travel for candidates in Texas.