Lead AI Engineer, End-to-End Solutions

Groundswell; New York (NY); On-site; USD 88,000 - 172,000; Full time

Benefits offered by this job

Medical/dental/vision

Flexible Spending Account

401K Match

Paid Time Off

Tuition reimbursement

Flexible work schedule

On-site gym

Childcare option

Job summary

Groundswell is seeking an AI-enabled software engineer to own capabilities end to end, from discovery to production. You will define technical approaches, select AI patterns, and build complete solutions, balancing accuracy, cost, latency, and scalability while guiding engineers and executives.

You will mentor others, maintain current AI tooling knowledge, and operate across client delivery, internal products, and rapid proofs of concept in a fast-paced, audacious environment.

Qualifications

• 4+ years building and shipping production software.
• At least 1 year of hands-on experience building applications that integrate large language models, including prompt engineering, retrieval-augmented generation, structured extraction, tool use, and agent patterns.
• Demonstrated ability to own a capability end to end, from an ambiguous requirement through to something in production that people rely on.
• Demonstrated experience evaluating AI system quality, including building test sets, defining metrics, and making deployment decisions based on evidence.
• Strong programming ability in Python, TypeScript, or SQL, applied across the full application rather than the AI layer alone.
• Proven ability to lead requirements conversations with non-technical stakeholders and translate what you hear into a technical approach.
• Sound judgment on tradeoffs between accuracy, cost, latency, and complexity, with the ability to explain those tradeoffs to both engineers and executives.
• Excellent written communication. This role produces client-facing documentation and design rationale.
• Ability to work independently through ambiguous requirements, define an appropriate technical approach, and drive work through implementation and delivery with minimal direction.
• Willingness to teach. Developing the capability of others is valued alongside individual delivery.
• U.S. Citizenship required.

Responsibilities

• Lead requirements conversations with business and technical stakeholders, covering the workflow, the decision being supported, the current standard for acceptable results, and the constraints that were not raised initially.
• Define the technical approach and defend it. Select the AI pattern appropriate to the problem, such as extraction, classification, summarization, retrieval, or an agentic workflow, rather than defaulting to the most sophisticated option available.
• Recommend against AI when a simpler solution is the right one. Rules, process changes, and improved interfaces are often the correct answer, and identifying that early is part of the job.
• Define measurable success criteria before building, including accuracy targets, human review thresholds, acceptance conditions, and the definition of failure.
• Build the complete capability rather than the AI components alone. This includes prompt and retrieval design, structured outputs, tool and function definitions, API integration, data handling, error states, and the user interface.
• Build evaluation sets from real data and measure against them. Iterate based on results rather than intuition, and determine when a capability is ready for deployment.
• Make and defend architecture decisions within your scope, weighing quality, cost, latency, security, and authorization constraints.
• Operationalize capabilities in the client environment, including governance, logging, and traceability requirements. Monitor quality, cost, latency, and drift after launch, and optimize as better or less expensive options become available.
• Help clients understand what is possible. Anticipate needs, shape the next phase of work, and build the proof of concept that supports the case.
• Mentor engineers who are new to AI through code review, pairing, and guidance toward the appropriate pattern for a given problem.
• Maintain current knowledge of models, tooling, and techniques, and bring back what proves useful as reusable patterns for the team.
• Move between client delivery, internal product work, rapid proofs of concept, and internal enablement as the work requires.
• Produce clear documentation covering what the solution does, its known limitations, how it was validated, and what happens when it produces an incorrect result.

Skills

LLM integration

Prompt engineering

End-to-end ownership

AI evaluation & metrics

Python/TypeScript/SQL

Stakeholder communication

Independent work

Teaching capability

Security clearance readiness

Cost/latency tradeoffs

Architectural decision making

Education

Master's degree

Tools

Appian

OutSystems

Mendix

Power Platform

ServiceNow

Salesforce

AWS Bedrock

Azure OpenAI

Job description

Groundswell is seeking an AI-enabled software engineer to own capabilities end to end, from discovery to production. You will define technical approaches, select AI patterns, and build complete solutions, balancing accuracy, cost, latency, and scalability while guiding engineers and executives.

You will mentor others, maintain current AI tooling knowledge, and operate across client delivery, internal products, and rapid proofs of concept in a fast-paced, audacious environment.