LLM Platform Engineer/Lead

Accountabilities:
The LLM Platform Engineer/Lead will be responsible for creating scalable AI infrastructure and enabling teams to successfully integrate AI capabilities into products and workflows. The role requires a balance of technical execution, platform ownership, and internal AI advocacy. Design and develop internal AI frameworks, SDKs, and shared libraries to simplify LLM adoption across teams. Build reusable components for prompt management, evaluation, observability, monitoring, and AI safety. Establish standardized approaches for using LLMs, embeddings, agents, and AI workflows. Enable engineering teams to integrate AI-powered features with minimal complexity and strong production readiness. Own AI tooling initiatives from early experimentation through deployment and continuous improvement. Define best practices around AI usage, reliability, scalability, and cost optimization. Create documentation, examples, and technical guidance to promote AI adoption internally. Prototype AI-driven solutions and convert successful experiments into reusable platform capabilities. Support the development of AI-powered features through strong engineering practices and system design expertise. Requirements: The ideal candidate brings strong software engineering experience combined with hands-on expertise in AI platforms, LLM technologies, and developer enablement. They should be comfortable working in fast-changing environments and translating complex AI concepts into practical solutions. Several years of professional software engineering experience, with strong experience building platforms, frameworks, or developer tools. Excellent Python programming skills and experience delivering production-grade software. Hands-on experience with LLM APIs such as OpenAI, Anthropic, or similar AI platforms. Practical experience building RAG pipelines, working with embeddings, vector databases, and AI orchestration frameworks. Experience with prompt engineering, prompt versioning, evaluation methodologies, and AI observability. Proven experience shipping at least one LLM-powered feature into production and improving it through telemetry and user feedback. Strong understanding of APIs, service architecture, distributed systems, and scalable application design. Familiarity with embeddings, fine-tuning concepts, vector search, tokenization, and AI evaluation techniques. Experience working with cloud platforms such as AWS, GCP, or Azure, as well as modern data infrastructure including data lakes, warehouses, or ETL pipelines. Ability to create simple and reusable abstractions from complex AI technologies. Strong ownership mindset, adaptability, and willingness to experiment, iterate, and deliver quickly. Nice-to-have experience with agent-based systems, tool calling, AI guardrails, moderation systems, Docker, CI/CD, or platform engineering. Benefits: Direct collaboration with senior leadership and high visibility across the organization. Opportunity to shape AI strategy and build impactful solutions from the ground up. Strong career growth and continuous learning opportunities in a rapidly evolving technology space. Chance to work with an experienced international team of engineers, entrepreneurs, and technology professionals. Flexible remote work arrangement. Home office setup budget to support a productive remote environment. Annual learning and development budget for professional growth. A collaborative and innovative culture focused on experimentation and impact.

How Jobgether works:
We use an AI-powered matching process to ensure your application is reviewed quickly, objectively, and fairly against the role's core requirements. Our system identifies the top-fitting candidates, and this shortlist is then shared directly with the hiring company. The final decision and next steps (interviews, assessments) are managed by their internal team.
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