AI Engineer

Sambla

. Design and evolve architecture for LLM-based products such as chatbots, copilots, automation, and retrieval on AWS .

Posted 10/27/2026full-timeStockholm • SwedenMid-LevelSeniorWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in designing and evolving architecture for LLM-based products, integrating them into backend systems, and optimizing AI services for performance and cost. Proven ability to collaborate across teams while taking technical ownership of AI initiatives and ensuring compliance with data privacy and security standards.

Highest-signal resume keywords

LLM/GenAI Systems ExperienceBackend Engineering in TypeScript/PythonCloud-Native Architecture on AWSMLOps/LLMOps FamiliaritySystem-Design Skills for Microservices

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

LLM-Based Product DesignPython ProgrammingTypeScript ProgrammingMLOps ConceptsRAG PipelinesPrompt EngineeringAI/ML Feature ShippingSystem DesignData Flow IntegrationMonitoring and Alerting

Soft Skills

CollaborationMentoringInfluencing Without AuthorityCommunication of Complex Concepts

Tools & Technologies

AWSContainersServerless ArchitectureCI/CD ToolsLogging and Monitoring ToolsSDKsLibrariesReference Services

Industry Keywords

Data PrivacyAccess ControlPrompt Injection ProtectionAI ComplianceEnterprise SearchKnowledge GraphsData Management

Tech Stack

Tools & technologies

AWSCloudPythonTypeScript

About the role

Key responsibilities & impact

Design and evolve architecture for LLM-based products such as chatbots, copilots, automation, and retrieval on AWS
Integrate LLM-based products into Python/TypeScript backend systems
Define reusable patterns for RAG, prompts, agents, and evaluation
Work with Security and Legal on data privacy, access control, and provider/model choices
Build protections against prompt injection and unsafe outputs
Integrate AI services with monitoring and alerting
Optimize prompts, caching, and model selection to balance quality, latency, and cost
Create and maintain SDKs, libraries, templates, and reference services for product teams
Align ML and LLM pipelines with MLOps
Take end-to-end technical ownership of high-impact AI initiatives from prototyping and design through implementation, rollout, and continuous improvement
Create technical plans and de-risk new approaches through prototypes
Collaborate with Product & Tech, Data Science, MLOps, Security, Legal, and backend teams

Requirements

What you’ll need

5+ years in software and/or ML engineering
At least 2–3 years working with LLM/GenAI systems in production
Proven track record of shipping AI/ML/LLM-powered features to end users and operating them in production
Strong backend engineering skills in TypeScript and/or Python
Comfortable with cloud-native architecture on AWS, including containers/serverless, networking, IAM, and logging/monitoring; ideally including Bedrock
Deep practical experience with hosted and/or self-hosted models, RAG pipelines, prompt patterns, guardrails, and evaluating/improving LLM outputs
Familiarity with MLOps/LLMOps concepts and tooling, including CI/CD, model and prompt versioning, experiment tracking, monitoring, and data pipelines
Strong system-design skills for services/APIs, data flows, and integrations in a microservice/product architecture
Comfortable operating as an experienced individual contributor, setting technical direction, influencing without line authority, and mentoring others
Experience working closely with product managers, data scientists, and engineers, and explaining complex trade-offs clearly
Nice to have: experience from an AI-mature, product-led company using AI/LLMs at scale
Nice to have: exposure to enterprise search, ingestion pipelines, knowledge graphs, ontology/tagging, or other data/knowledge-management for LLMs
Nice to have: data science/ML background sufficient to discuss classic ML vs LLMs vs rules
Nice to have: experience with security and compliance for AI in regulated or semi-regulated environments

Benefits

Comp & perks

Background check conducted before extending a job offer
Opportunity to work on next-generation AI financial products
Mentoring and collaboration with Product, Tech, Data Science, MLOps, Security, Legal, and backend teams