Senior AI Platform Engineer

Accountabilities:

• • Design and build reusable platform capabilities supporting LLM applications, AI agents, RAG, tool calling, and AI workflows.
  • Develop scalable data and knowledge pipelines covering ingestion, embeddings, retrieval, vector search, metadata, and knowledge management.
  • Build secure integrations between AI applications, enterprise data, and business systems using APIs, MCP, tool calling, and comparable integration patterns.
  • Develop reusable frameworks, libraries, services, SDKs, and developer tooling that allow engineering teams to build AI applications efficiently.
  • Establish technical standards and patterns for AI deployment, observability, evaluation, monitoring, and lifecycle management.
  • Define and improve approaches for measuring AI quality, accuracy, latency, reliability, security, and cost.
  • Take AI capabilities from experimentation and prototyping through reliable, scalable, and maintainable production deployment.
  • Ensure AI platform capabilities comply with security, privacy, authentication, authorization, access control, and data governance requirements.
  • Work with Engineering, Product, and IT teams to identify AI opportunities and translate ambiguous business needs into practical technical solutions.
  • Evaluate emerging AI models, frameworks, infrastructure technologies, and development approaches to determine their potential business value.
  • Contribute to the evolution of platform architecture, engineering standards, and reusable AI capabilities across the organization.
  • Promote strong data engineering and platform practices around data modeling, data quality, secure data access, and operational reliability.

  Requirements:

  • 5+ years of professional experience in data engineering, platform engineering, backend engineering, or a closely related discipline, with experience building and operating production systems.
  • Strong proficiency in Python and SQL.
  • Hands-on experience building and deploying production applications or services using LLMs and generative AI.
  • Practical experience with RAG, embeddings, vector search, tool/function calling, AI agents, or enterprise knowledge systems.
  • Strong data engineering fundamentals, including data pipelines, data modeling, data quality, and secure data access.
  • Experience with Snowflake, Databricks, or comparable modern data platforms, together with tools such as dbt, Airflow, or similar technologies.
  • Proven experience building shared AI infrastructure, platforms, or reusable AI capabilities rather than focusing exclusively on individual AI applications.
  • Experience taking AI systems from experimentation and proof of concept through reliable production deployment.
  • Solid understanding of security, authentication and authorization, privacy, access control, and data governance.
  • Strong ability to translate ambiguous AI opportunities into practical, scalable engineering solutions.
  • Excellent communication and collaboration skills, with the ability to work effectively across Engineering, Product, IT, and other stakeholders.
  • Experience with AWS Bedrock or other managed foundation-model platforms is an advantage.
  • Hands-on experience with MCP (Model Context Protocol) or comparable approaches for connecting AI systems to enterprise tools and data is a plus.
  • Experience with LLM evaluation, AI observability, monitoring, quality management, or responsible AI practices is desirable.
  • Familiarity with LangChain, LangGraph, vector databases, search technologies, internal developer platforms, SDKs, APIs, or reusable infrastructure is a strong advantage.
  • Fluent professional proficiency in English, both written and spoken.
  • Reliable home internet connection suitable for fully remote work.

  Benefits:

  • Fully remote working environment with the flexibility to work from home.
  • Ability to work from anywhere within the permitted employment locations and time-zone range, subject to local work authorization.
  • Approximately 40 days of paid time off per year, including holidays and vacation, or more where required by local regulations.
  • Mental health and wellbeing support.
  • Monthly wellbeing allowance that can be used across a broad range of eligible services and activities.
  • Flexible parental leave, including at least three months of paid leave for new parents, subject to applicable local requirements.
  • Work-from-home stipend for laptop and home office equipment.
  • Fully distributed, international working environment with colleagues across multiple countries and cultures.
  • Asynchronous and collaborative working model designed for globally distributed teams.
  • Opportunity to work on foundational AI infrastructure and influence how AI applications are developed and operated at scale.
  • Exposure to cutting-edge technologies including LLMs, AI agents, RAG, MCP, vector search, modern data platforms, and AI governance.
  • Strong opportunity for technical ownership, professional growth, and meaningful impact within a global technology organization.
  • Inclusive and diverse environment that values autonomy, trust, collaboration, and continuous progress.