About Nymbl
Nymbl is an advisory and development agency that helps companies create custom products and solutions using a diverse set of technologies from no-code / low-code, to full stack to AI. We employ an iterative Learn, Build and Grow model that helps us move fast, and build long-term strategic relationships with our clients. Join the future of software development as an AI Solutions Engineer in our quickly growing company!
About the Role
The AI Solutions Engineer at Nymbl delivers enterprise-grade AI solutions directly with clients. Acting as a forward-deployed engineer, this role blends full-stack development expertise, applied AI/ML engineering, and strong client-facing skills. AI Solutions Engineers implement Retrieval-Augmented Generation (RAG) systems, design and deploy LLM-powered applications, and integrate AI into enterprise workflows to create measurable client outcomes.
This role is ideal for engineers who enjoy architectural ownership, technical decision-making under uncertainty, and multiplying their impact through others.
What You Will Do
- Build and deploy AI-powered applications using LLMs in production environments
- Design and implement Retrieval-Augmented Generation pipelines connected to enterprise data sources
- Develop AI agents and workflows using frameworks such as LangChain, Semantic Kernel, or custom solutions
- Build full-stack applications including APIs, backend services, and user-facing interfaces
- Integrate AI solutions into existing enterprise systems and workflows
- Optimize prompts, retrieval strategies, and model behavior for reliability and accuracy
- Deploy and operate AI systems using Docker, Kubernetes, and CI/CD pipelines
- Participate in client workshops, demos, and delivery reviews as a technical contributor
- Document architectures, implementations, and operational guidance for clients and internal teams
Skills and Experience
- 2-4+ years of professional software engineering experience
- 2+ years building and deploying AI or LLM-powered applications in production environments
- Strong software engineering fundamentals with production experience
- Deep expertise in designing and deploying Retrieval-Augmented Generation systems end-to-end
- Advanced prompt engineering and context engineering techniques for reliable, enterprise-grade outputs
- Architecting and implementing AI agent systems with reasoning, planning, tool use, and error recovery
- Strong judgment in model selection, cost optimization, latency reduction, and performance tradeoffs
- Production integration with LLM APIs such as OpenAI, Azure OpenAI, and Anthropic
- Experience with observability tools such as LangSmith, Langfuse, or equivalent custom solutions
- Strong software engineering fundamentals with full-stack experience
- Exceptional ability to communicate complex technical concepts to executives, engineers, and clients
Nice To Haves
- Demonstrated ownership of complex systems from design through production
- Experience fine-tuning or adapting LLMs using supervised fine-tuning, RLHF, or domain adaptation approaches
- Strong mentorship and technical leadership skills
- Experience influencing technical direction across teams without direct authority
- Experience delivering enterprise-grade solutions in client-facing or high-stakes environments
