AI Solutions Engineer

Job 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: Forward-Deployed Engineer– client delivery, technical advisory, building in production environments. Machine Learning Engineer– Fine tune, and deploy LLM and RAG systems with applied AI expertise. Full-Stack Developer– enterprise-grade coding across front-end, back-end, and data layers. Your Responsibilities • Collaborate with clients to understand their business requirements and translate them into technical specifications. • Design and implement scalable solutions using cloud technologies such as AWS. • Develop machine learning models utilizing frameworks like TensorFlow, R, and Python for various applications including natural language processing and data mining. • Perform ETL processes to integrate data from multiple sources into cohesive datasets for analysis. • Conduct model training and deployment, ensuring optimal performance of machine learning applications. • Utilize big data technologies such as Hadoop and Spark to process large datasets efficiently. • Engage in database design and management, ensuring data integrity and accessibility using SQL and other database languages. • Implement analytics solutions using tools like Looker for data visualization and reporting. • Stay updated on industry trends, emerging technologies, and best practices in quantum engineering and AI. 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. What You'll Do • Designand implement RAG pipelines with LLMs and enterprise data sources. • Buildand deploy AI agents using frameworks such as LangChain, Semantic Kernel, or custom architectures. • Developfull-stack AI-enabled applications (front-end, back-end, APIs, and data integrations). • Optimizevector databases (e.g., Pinecone, FAISS, Milvus) for retrieval and semantic search. • Fine-tune or adaptLLMs for industry- or client-specific needs. • Deploysolutions with enterprise reliability standards (Docker, Kubernetes, CI/CD). • Run client demos,technical workshops, and enablement sessions to accelerate adoption. • Collaboratewith internal teams on burn tracking, utilization, and project profitability. • Documentarchitectures, pipelines, and operational guidelines for client and internal use. 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. Job Type: Full-time Benefits: • 401(k) • Dental insurance • Health insurance • Paid time off Application Question(s): • LinkedIn Link Required for Candidacy Work Location: Remote