Forward Deployed Solution Engineer (Applied AI)

Join our team as a Forward Deployed Solution Engineer (Applied AI) and take on the role of CTO for our cutting-edge AI initiatives. You will design, implement, and deliver solution-ready builds in agile sprints, working closely with customers in the field. Your software will become the reference implementation for scalable GenAI in the enterprise, and you will have the opportunity to shape internal tooling and accelerate innovation. This role requires a systems-minded, AI-native engineer with a strong background in software engineering and a passion for delivering real impact.

- Act as the CTO of the build, owning everything from backend services to LLM pipelines and front-end integrations. - Partner with customers in the field to design, implement, and deliver solution-ready builds in agile sprints. - Codify reusable assets—libraries, prompts, scaffolds—to accelerate future engagements and shape developer experience.

- Platform mindset: Can contribute to shared SDKs and tools, raising engineering velocity for the whole org - LLM tooling: Familiarity with LangChain, Semantic Kernel, prompt chaining, vector search, and context management - Performance & observability: Skilled in debugging distributed systems, tuning for latency, and implementing monitoring - Product sensibility: Prioritize for user value, MVP iteration, and long-term scale - Field readiness: Able to travel up to 30% to embed onsite and deliver where it matters - Machine Learning: Foundations of MLflow, Weights & Biases, LoRA, PEFT, SageMaker, Vertex AI, AzureML, Kubeflow, Airflow, Prefect, TorchServe, Triton, vLLM, SHAP, LIME, pandas, Spark, SQL - Relevant Experience: 10+ years of software engineering, including 2+ years building systems in customer-facing or embedded roles - Engineering depth: Strength in backend (Python, Node.js, Java), frontend (React, Angular), APIs (REST/GraphQL) - DevOps fluency: Experience deploying in AWS, Azure, or GCP with CI/CD, containers, and infra-as-code - System architecture: Proven ability to design and implement AI-native software in production environments - Experience: In leveraging or critically thinking about how to integrate AI into work processes, decision-making, or problem-solving. This may include using AI-powered tools, automating workflows, analyzing AI-driven insights, or exploring AI’s potential impact on the function or industry - Contributions to dev experience tooling, frameworks, or reusable AI scaffolds - Track record of production deployments in secure, regulated enterprise environments - Experience integrating AI into SaaS platforms like ServiceNow or Salesforce