New opportunity

Strategic AI FDE Enablement Operations Engineer

Cove Hill Partners · New York City, United States

About this role

This forward-deployed AI engineering role establishes and scales AI capabilities across a portfolio of companies. The engineer works with executives and their teams to identify opportunities, deliver production solutions, and help internal teams independently build and scale AI-enabled products and workflows. Potential projects span product development, engineering productivity, sales enablement, customer onboarding, and support.

Responsibilities

Partner with executive, technical, and functional leaders to prioritize high-impact AI opportunities across product, sales, finance, customer support, and operations.
Design, build, and deploy AI-enabled products, agents, and workflows. Turn loosely defined business challenges into solutions, from rapid prototypes through production deployment.
Integrate solutions with CRM and ERP systems, data warehouses, APIs, and legacy data environments. Assess AI readiness, architecture, data needs, and implementation considerations.
Support adoption through training, documentation, change management, and collaboration with business and technology teams. Create reusable frameworks, playbooks, tools, and best practices.
Advise senior leaders on AI strategy, technical tradeoffs, risks, and emerging capabilities. Coach engineers and business leaders to develop lasting internal capabilities.

Qualifications

5+ years of software engineering, technical architecture, AI engineering, or related experience, including building and launching production solutions.
Hands-on experience with large language model APIs, AI agents, RAG architectures, automation platforms, and related development frameworks; experience taking products or technical solutions from concept to production, including 0-to-1 initiatives.
Strong enterprise systems integration experience and the ability to navigate unfamiliar technologies, processes, and fragmented data environments. Ability to balance rapid delivery with production rigor, work independently across multiple ambiguous initiatives, communicate with technical and non-technical stakeholders, advise executives, and coach internal teams.

Location: New York City, New York. Listed salary: USD $225,000–$300,000 per year.

For full details about this role, click “Apply on company site” to view the employer’s original posting.

Skills for this role

AI engineeringLarge language model APIsAI agentsRAGAutomation platformsSoftware engineeringTechnical architectureSystems integrationCRMERPData warehousesAPIsPrototypingProduction deploymentAI strategyChange managementTechnical documentationExecutive communicationCoaching

Checking your profile…

Preparation suggestions, not the employer’s actual interview questions. Always check the original posting for current requirements.

Understand the role

What matters most

Prioritize AI opportunities with leaders
The posting calls for partnering with executive, technical, and functional leaders to identify high-impact opportunities across business functions. For preparation, frame how you would compare potential value, feasibility, data readiness, and implementation effort before selecting an initiative.
Deliver AI solutions from concept to production
The role owns AI-enabled products, agents, and workflows from loosely defined problems and rapid prototypes through deployment. Prepare to explain a 0-to-1 delivery approach, including where LLM APIs, agents, or RAG would—and would not—fit.
Assess readiness and integrate enterprise systems
The posting emphasizes architecture and data assessment, plus integration with CRM, ERP, data warehouses, APIs, and legacy environments. Prepare to walk through how you would map dependencies and choose an implementation approach when systems are unfamiliar.
Build adoption and internal independence
Training, documentation, change management, and coaching engineers and business leaders are explicit duties. Prepare to describe how a delivered solution could become something an internal team can operate, improve, and scale.
Create reusable approaches across the portfolio
The role is intended to scale AI capabilities across portfolio companies through frameworks, playbooks, tools, and best practices, while advising senior leaders on tradeoffs and risks. Prepare to distinguish what can be standardized from what must be tailored to each organization.

Likely challenges to think through

Speed versus production rigor
Likely challenge, not a confirmed employer problem: rapid prototypes may face different demands than production systems. Prepare to explain decision points for moving from experimentation to deployment, including how you would evaluate quality, risks, and scalability without losing momentum.
Fragmented data and unfamiliar systems
Likely challenge: the stated mix of legacy environments, enterprise applications, and fragmented data can make an appealing AI use case difficult to implement. Prepare an approach for discovering data constraints early and adjusting scope or architecture accordingly.
Portfolio reuse versus local fit
Likely challenge: reusable tools can accelerate work across companies, but their processes and technical foundations may differ. Prepare to explain what you would make common, what you would adapt, and when a shared approach is not worth imposing.
Hands-on delivery versus team ownership
Likely challenge: directly building solutions can speed execution, while the role also aims to leave internal teams able to build and scale independently. Prepare to describe how you would divide work, transfer knowledge, and judge when to step back.

Practice your answers

Technical practice

How would you prioritize AI opportunities across several portfolio companies?

What to coverSuggested preparation: Explain how you would compare business value, feasibility, data readiness, and implementation effort with executive and functional leaders. Describe what evidence would justify a prototype versus deferring an idea.

How would you assess whether a team is ready to implement an AI solution?

What to coverSuggested preparation: Cover the existing architecture, available data, integration constraints, and the team's ability to operate the solution. Explain how findings would change the proposed scope or technical approach.

How would you design an AI-enabled product using a large language model API?

What to coverSuggested preparation: Walk through the product need, where the model adds value, and how the application would handle model output and failures. Discuss trade-offs among response quality, latency, cost, and production reliability.

When would you use RAG, and how would you design it for fragmented company data?

What to coverSuggested preparation: Explain when retrieval is preferable to relying on a model's general knowledge, then describe data preparation, retrieval, and answer grounding. Discuss how you would test whether retrieved material is relevant and current.

When does an AI agent make sense instead of a simpler workflow?

What to coverSuggested preparation: Compare an agent's flexibility with the predictability of a defined automation. Explain how you would bound its actions and evaluate whether the added complexity improves the business process.

How would you integrate an AI workflow with CRM, ERP, APIs, or legacy data?

What to coverSuggested preparation: Describe how you would map the required data and system interactions before building. Address inconsistent records, access boundaries, and how to handle an integration failure without disrupting the underlying business process.

How would you take a loosely defined AI idea from prototype to production?

What to coverSuggested preparation: Describe how you would turn the business challenge into a testable scope, validate a rapid prototype, and decide what must change before launch. Explain the trade-off between delivery speed and production rigor.

How would you diagnose an AI workflow that works in demos but fails in real use?

What to coverSuggested preparation: Separate possible problems with inputs, retrieval, model behavior, and system integrations, then explain how you would gather evidence to isolate the cause. Discuss how you would evaluate a fix against the intended business outcome.

What would you make reusable across AI initiatives without over-standardizing them?

What to coverSuggested preparation: Consider which architectural patterns, tools, documentation, or playbooks could accelerate work across companies. Explain how you would leave room for differences in data, systems, and business needs.

How would you assess an emerging AI capability for a practical business use?

What to coverSuggested preparation: Start with a meaningful product or operational problem rather than the novelty of the technology. Explain how you would compare the new capability with an existing approach and present its value, trade-offs, and risks to senior leaders.

Behavioural practice

Tell me about a time you delivered a solution despite an ambiguous brief.

What to coverSuggested preparation: Use a real situation, the actions you took to clarify the problem and move from concept toward delivery, and the result. Highlight how you decided what to validate first.

Describe a time you challenged a senior leader's proposed technical approach.

What to coverSuggested preparation: Give the situation, how you explained the business and technical trade-offs, and the result. Focus on how you challenged assumptions constructively rather than relying on authority.

Tell me about a time you influenced teams without formal authority.

What to coverSuggested preparation: Describe the stakeholders' differing priorities, the actions you took to build agreement, and the outcome. Choose an example involving technical and non-technical perspectives if you have one.

Describe a time you helped a team adopt and independently maintain a new capability.

What to coverSuggested preparation: Explain the starting situation, your hands-on work and any training or documentation you provided, and the result. Distinguish delivery of the solution from development of the team's lasting capability.

Tell me about a time you managed several fast-moving initiatives at once.

What to coverSuggested preparation: Set out the competing demands, how you prioritized and communicated trade-offs, and what resulted. Include a specific decision to narrow, sequence, or change work rather than a general account of staying organized.

Sharpen your knowledge

Topics to prepare

Prioritizing AI opportunities
The posting calls for identifying high-impact opportunities with executive, technical, and functional leaders. Practice suggestion: compare two hypothetical opportunities, such as sales enablement and customer support, using business value, data readiness, delivery effort, and risk; explain which to pursue first.
Choosing among LLM, RAG, and agent designs
Hands-on experience with LLM APIs, AI agents, and RAG architectures is listed. Practice suggestion: for a hypothetical customer-support workflow, explain when a direct model call, retrieval from company content, or an agentic workflow would be appropriate, including the tradeoffs.
Enterprise AI readiness and architecture
The role evaluates data requirements and integrates with CRM, ERP, data warehouses, APIs, and legacy environments. Practice suggestion: draw a conceptual data-flow diagram for a fragmented-data scenario and identify questions to resolve before selecting an architecture.
Production rigor and adoption
The posting emphasizes moving from rapid prototypes to production while supporting adoption through training, documentation, and change management. Practice suggestion: outline the technical risks and team handoffs you would review before releasing a hypothetical AI workflow.

Skills to revise

Build a small LLM workflow
The posting requires hands-on work with LLM APIs, agents, RAG, and automation platforms. Practice suggestion: implement a small workflow using sample documents and an LLM API, then demonstrate how it retrieves information, handles an unanswered question, and could be extended with an agent or automation platform.
Map an enterprise integration
Integrating AI solutions with enterprise applications, APIs, data platforms, and fragmented data is an explicit responsibility. Practice suggestion: use mock CRM and data-warehouse records to map identifiers, data movement, access needs, and failure points for one AI-enabled workflow.
Plan a prototype-to-production transition
The role takes ambiguous initiatives from concept through deployment and balances speed with production rigor. Practice suggestion: turn a hypothetical prototype into a staged delivery plan with acceptance criteria, testing, operational ownership, and a rollback approach.
Present a decision to senior leaders
The posting calls for advising executives on strategy, tradeoffs, and risks in business terms. Practice suggestion: deliver a brief recommendation for one hypothetical AI opportunity, stating expected value, key assumptions, alternatives, and a decision needed.
Create a reusable handoff
Training, documentation, playbooks, and coaching internal teams are stated responsibilities. Practice suggestion: write a one-page handoff for the sample workflow covering its purpose, limitations, maintenance steps, and what the internal team would need to operate it independently.

Your preparation checklist

Prepare a concept-to-production walkthrough
The posting calls for production launches, including 0-to-1 initiatives. Prepare one project account covering the ambiguous starting point, your decisions, prototype, deployment, results, and what you would change.
Practice prioritizing AI opportunities
The role partners with executives and functional leaders across portfolio companies. As a preparation exercise, draft a one-page comparison of two possible use cases, stating business value, data readiness, implementation effort, and key assumptions.
Sketch an AI solution and its integrations
The posting names LLM APIs, agents, RAG, automation, and connections to CRM, ERP, warehouses, APIs, and legacy data. Draw a sample architecture showing which AI approach you would choose, the data flow, and integration constraints; label it as a hypothetical design.
Explain the path from prototype to reliable deployment
Prepare a short design review showing how you would test a prototype, handle failure and data-access risks, and decide when it is ready for production. These are suggested discussion points for the posting’s speed-versus-production-rigor requirement.
Show how you would transfer capability to internal teams
The role includes training, documentation, reusable playbooks, and coaching. Outline a sample handoff with an operator guide, a reusable template, and a plan for engineers and business teams to take ownership.
Write questions about delivery and ownership
Bring questions about how opportunities are prioritized across portfolio companies, who owns deployed solutions, what integration constraints recur, and how the team judges whether internal AI capability has grown.

Availability last checked: Nov 4, 2026. Status can change after a check; confirm on the employer’s website. Report an issue · Our checking policy

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