Forward Deployed Solution Engineer – Applied AI

This is a fully remote position, open to applicants in United Kingdom.

📋 Description

• Develop solution-ready applications that leverage LLM technology, encompassing backend logic, data orchestration, and front-end UI.

• Collaborate closely with customers in real-world settings to adapt, deploy, and refine solutions in live environments.

• Deliver production-ready solutions through agile, end-to-end sprints.

• Engineer APIs, orchestration pipelines, vector databases, LLM frameworks, and UI components.

• Integrate with legacy systems, handle ambiguity, and ensure safe and rapid deployment.

• Create reusable scaffolds, SDKs, libraries, prompts, and comprehensive documentation.

• Provide feedback to platform and product teams and help shape product strategy.

• Troubleshoot distributed systems, optimize latency, and implement monitoring solutions.

• Lead projects from inception to completion alongside architects, product managers, and customer teams.

• Convert successful customer solutions into reusable patterns for wider enterprise application.

⛳️ Requirements

• Experience in leveraging or critically evaluating the integration of AI into workflows, decision-making processes, or problem-solving methods.

• Over 10 years of experience in software engineering.

• More than 2 years of experience in building systems in customer-facing or embedded roles.

• Demonstrated ability to design and deploy AI-native software in production settings.

• Strong backend proficiency in Python, Node.js, and Java.

• Strong frontend skills in React and Angular.

• Experience with REST and GraphQL APIs.

• Knowledge of MLflow, Weights & Biases, LoRA, PEFT, SageMaker, Vertex AI, AzureML, Kubeflow, Airflow, Prefect, TorchServe, Triton, vLLM, SHAP, LIME, pandas, Spark, and SQL.

• Familiarity with LangChain, Semantic Kernel, prompt chaining, vector search, and context management.

• Proficient in debugging distributed systems, optimizing latency, and setting up monitoring.

• Ability to contribute to shared SDKs and tools.

• Product sensibility centered around user value, MVP iteration, and long-term scalability.

• Experience with deployments in AWS, Azure, or GCP, utilizing CI/CD, containers, and infrastructure-as-code.

• Willingness to travel up to 30% and work onsite when necessary.

• Preferred: experience integrating AI into SaaS platforms like ServiceNow or Salesforce.

• Preferred: experience with production deployments in secure, regulated enterprise environments.

• Preferred: contributions to developer-experience tools, frameworks, or reusable AI scaffolds.

🏝️ Benefits

• Flexible or remote work options.

• Opportunity to engage in transformative enterprise AI projects with strategic customers.

• Chance to influence internal tooling and platform/product strategy.

• Opportunity to develop reusable libraries, prompts, scaffolds, SDKs, and documentation.

• Commitment to an equal opportunity and inclusive workplace.

• Reasonable accommodations available during the application process.