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.