AI Solutions Engineer

About Nymbl

At Nymbl, we redefine application development—combining expertise and innovation to build next-generation solutions using AI, full stack, and low-code/no-code technologies. Our learn, build, grow model ensures long-term success for our clients while creating space for our people to thrive.

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Role Summary

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 blends responsibilities from:

1. Forward-Deployed Engineer – client delivery, technical advisory, building in production environments.
2. Machine Learning Engineer – Fine tune, and deploy LLM and RAG systems with applied AI expertise.
3. Full-Stack Developer – enterprise-grade coding across front-end, back-end, and data layers.

Expectations

• Leadership: Take ownership of technical implementation, guiding both clients and internal teams
• toward scalable, production-ready AI solutions.
• Communication: Translate complex AI concepts into clear business and technical language for
• executives, stakeholders, and developers.
• Autonomy: Lead end-to-end delivery of AI features and integrations, managing coding, testing,
• deployment, and client handoff.
• Collaboration: Partner closely with Solution Architects, Client Partners, and Developers to ensure
• projects balance innovation, feasibility, and business value.
• Client Engagement: Act as a trusted technical advisor in workshops, demos, and delivery reviews,
• building confidence that Nymbl can execute reliably.

Business-as-Usual Activities

• Design and implement RAG pipelines with LLMs and enterprise data sources.
• Build and deploy AI agents using frameworks such as LangChain, Semantic Kernel, or custom architectures.
• Develop full-stack AI-enabled applications (front-end, back-end, APIs, and data integrations).
• Optimize vector databases (e.g., Pinecone, FAISS, Milvus) for retrieval and semantic search.
• Fine-tune or adapt LLMs for industry- or client-specific needs.
• Deploy solutions with enterprise reliability standards (Docker, Kubernetes, CI/CD).
• Run client demos, technical workshops, and enablement sessions to accelerate adoption.
• Collaborate with internal teams on burn tracking, utilization, and project profitability.
• Document architectures, pipelines, and operational guidelines for client and internal use.

Key Performance Indicators (KPIs)

• Solution Adoption Rate: % of delivered AI solutions actively used by clients after 90 days
• Deployment Success Rate ≥ 95% of AI solutions deployed on time and functioning as expected
• Billable Utilization ≥ 100% weighted utilization target
• Client Satisfaction: measured through post-engagement surveys and renewal likelihood
• Reusability Index: number of frameworks, libraries, or components reused across engagements

What Success Looks Like (6–12 Months)

• Recognized by clients as a trusted technical advisor and partner.
• AI solutions delivered are in production use and driving measurable outcomes.
• Consistently anticipate and solve client technical blockers before they escalate.
• Contribute to Nymbl’s AI playbook by codifying reusable frameworks, deployment best practices, or
• reference architectures.
• Demonstrated ability to work across multiple platforms and stacks, deployable in diverse client
• environments.
• Internal teams rely on your expertise to elevate technical standards and accelerate delivery velocity.

Common Challenges and Needed Skills

• Enterprise data complexity: problem-solving, unstructured +structured data pipelines.
• Rapidly evolving AI tools: continuous learning, adaptability, maturity assessment.
• Client skepticism about AI: clear communication, proof points, framing business value.
• Balancing innovation vs production-readiness: disciplined testing, pragmatic engineering mindset.
• Integration into legacy systems: creativity, patience, full-stack development skills.

Technical Skills

  Prompt Engineering

• Crafting and iterating on prompts for LLMs to achieve consistent, accurate, and enterprise-ready outputs.
• Applying structured techniques to reduce variability and ensure responses align with client requirements.

   Retrieval-Augmented Generation (RAG) Systems

• Designing pipelines that integrate vector databases, embeddings, and prompt templates.
• Connecting enterprise data sources into LLM-powered workflows for context-rich responses.

   Model Fine-Tuning

• Applying supervised fine-tuning, reinforcement learning with human feedback (RLHF), or domain adaptation.
• Providing client-specific datasets to improve accuracy, compliance, and relevance.

   AI Agents

• Building autonomous agents that use reasoning + tools to act within client environments.
• Combining multiple LLM roles (e.g., planner, executor, validator) into reliable workflows.

   LLM Deployment

• Packaging and deploying LLM solutions into client production environments.
• Leveraging containerization, APIs, and deployment pipelines for scalability and security.

   LLM Optimization

• Applying quantization, distillation, caching, and latency reduction techniques.
• Balancing model performance, cost efficiency, and client SLAs.

   LLM Observability

• Implementing monitoring for model accuracy, bias, latency, and cost.
• Using tracing, dashboards, and evaluation frameworks to ensure reliability at scale.

Context Engineering

• Designing workflows that bring the right data (documents, memory, tools, databases) into prompts.
• Ensuring compliance, data governance, and high fidelity of client knowledge bases.