Staff AI Engineer | Remote | #1637

• Type of contract: Contract of Employment
• Working mode: Fully remote
• Salary: 35 – 37,5k PLN/month

About Our Client

Our client is a rapidly scaling global leader in Sports Tech, combining high-growth SaaS platforms with integrated payments processing ($13B+ processed annually). Having expanded from $150M to $400M in revenue with double-digit international growth across 20+ countries, they maintain a high-impact, wellness-focused culture.

Company Culture & Values

• Best Life: Great work begins with great people. Their culture is built on respect, trust, and belonging. They create an inclusive environment where every team member can bring their authentic self to work—because diverse perspectives drive innovation and meaningful impact.
• Growth Mindset: They are doers, thinkers, and dreamers. Your growth is their investment. Through continuous learning, mentorship, and professional development opportunities, they empower employees to reach new heights—personally and professionally.
• One Team: From day one, employees are part of a team that collaborates, celebrates, and cares. They move fast, support one another, and have fun along the way.

About the Team

The client is building a shared AI Enablement Platform that allows every product team across the company to ship AI-powered features quickly, safely, and at scale.

The goal is simple: remove the plumbing so product teams can focus on customer value instead of reinventing AI infrastructure. This platform provides the paved path for building production AI systems across the organization, including:

• Reusable APIs for chat, summarization, RAG, classification, and AI workflow execution.
• Agent orchestration patterns for multi-step reasoning, tool use, validation, and fallback handling.
• Evaluation and validation systems to measure output quality and prevent regressions.
• Observability, policy enforcement, cost controls, and production guardrails.
• Developer experience, documentation, templates, and reference implementations that help teams adopt AI faster.

They value an entrepreneurial mindset—rolling up their sleeves, acting fast, and learning together.

About the Role

As a Staff AI Engineer – AI Enablement Platform, you will be one of the core technical builders of the client’s AI platform.

This is a deeply hands-on individual contributor role. You will be expected to write production code, design and build reusable platform services, review technical designs, debug production issues, and partner directly with product teams to drive adoption.

You will also influence technical direction, establish engineering patterns, and help teams adopt the platform, but your credibility will come from building real systems, not just advising from the sidelines.

This role is not focused on ML research, data science, or training models from scratch. The client is looking for a strong platform/backend engineer with deep hands-on experience building production-grade LLM systems, APIs, orchestration layers, evaluation systems, and developer-facing tools. You will help turn AI from isolated experiments into a repeatable engineering capability across the entire enterprise.

What You’ll Do

Build the AI platform foundations

• Write and ship production code for core AI platform services, APIs, orchestration components, evaluation systems, SDKs, and internal developer tools.
• Design, build, and operate reusable AI platform capabilities such as chat APIs, RAG services, summarization, classification, semantic search, prompt/workflow execution, and agent orchestration.
• Build high-quality APIs, SDKs, templates, and reference implementations that product teams can adopt with minimal friction.
• Create production-ready orchestration patterns for retrieval, tool use, validation, fallback handling, memory/state management, and human-in-the-loop workflows.
• Stay close to implementation details by reviewing PRs, debugging production issues, improving reliability, and making pragmatic technical tradeoffs with the team.
• Partner with SRE, Security, Platform, and Product Engineering teams to ensure the platform is reliable, scalable, secure, observable, and cost-aware.

Own evaluation, reliability, and production readiness

• Build evaluation harnesses for LLM-powered systems, including curated test sets, scenario-based evaluations, regression checks, quality gates, and release criteria.
• Implement AI observability practices such as tracing, prompt/version tracking, output quality monitoring, latency tracking, token/cost visibility, and production feedback loops.
• Design safe defaults for AI systems, including structured outputs, tool permissions, prompt-injection awareness, data handling controls, fallback paths, and failure-mode handling.
• Ensure AI capabilities are built with strong production fundamentals: testing, monitoring, rollout strategy, incident readiness, performance tuning, and cost management.
• Help teams move from prototypes to production by identifying gaps in reliability, observability, security, evaluation, and operational readiness.

Drive AI adoption across the company

• Establish internal standards for building production AI systems through patterns, standards, documentation, code examples, architecture reviews, and technical enablement.
• Work directly with product teams to help them adopt the platform, unblock implementation challenges, and turn early AI ideas into production-ready features.
• Lead technical design reviews and architecture forums for AI systems, ensuring teams make sound decisions around quality, safety, reliability, and maintainability.
• Identify repeated friction across teams and convert it into reusable platform capabilities.
• Help raise AI engineering capability across the company through internal demos, enablement sessions, office hours, technical write-ups, and engineering blog posts.

Lead through technical influence

• Act as a hands-on technical leader and multiplier, not just an advisor.
• Set technical direction for complex AI platform areas and align stakeholders across Product, Engineering, Security, SRE, and Data.
• Mentor engineers on production AI engineering practices, backend design, system reliability, evaluation strategy, and AI-native development workflows.
• Help teams use AI-assisted engineering tools responsibly to improve development speed, testing, debugging, documentation, and iteration without compromising quality.

What This Is Not

• Not a data science role focused on analysis, experimentation, or dashboards.
• Not an ML research role focused on training foundation models from scratch.
• Not a prompt-only role without ownership of production systems.
• Not building one-off AI features for a single product line.
• Not an architecture-only role where you primarily create diagrams, review designs, or delegate implementation to others.
• Not a people-management role. You will influence, mentor, and lead through technical depth, but this is an individual contributor position.

What You’ll Need

• Significant hands-on engineering experience, typically 10+ years, with strong backend/platform engineering depth.
• Recent hands-on experience building and operating production backend/platform systems, with full comfort going deep into code, APIs, infrastructure, observability, debugging, and production tradeoffs.
• Proven experience building and shipping production-grade AI/LLM systems such as RAG, agent workflows, tool-calling systems, AI APIs, or LLM-powered product capabilities.
• Strong programming experience, preferably in Python and/or backend service stacks used for production APIs and distributed systems.
• Deep understanding of API design, service boundaries, SDKs, integration patterns, reliability, testing, observability, performance, and cost optimization.
• Practical experience with LLM application architecture: context engineering, retrieval patterns, tool use, structured outputs, orchestration, fallback handling, and evaluation.
• Ability to build evaluation and validation systems for AI applications, including golden datasets, scenario-based tests, regression checks, and quality gates.
• Experience deploying and operating cloud-based production systems on AWS, GCP, Azure, or similar platforms.
• Strong technical judgment and ability to make tradeoffs across speed, reliability, safety, cost, developer experience, and business impact.
• Ability to lead through hands-on technical contribution: writing code, creating reference implementations, reviewing designs, mentoring engineers, and turning ambiguous platform needs into working systems.
• Proven ability to influence across teams through architecture reviews, design documents, technical standards, mentorship, and hands-on partnership.

And It’s Great To Have

• Experience building internal developer platforms, SDKs, shared services, or paved-path tooling.
• Hands-on experience with LLM observability/evaluation tooling such as Langfuse, LangSmith, OpenTelemetry-based tracing, or similar tools.
• Familiarity with LangChain, LangGraph, LlamaIndex, Semantic Kernel, or custom orchestration frameworks.
• Experience with vector databases, embedding workflows, semantic search, retrieval tuning, and RAG productionization.
• Experience with AI-native engineering workflows using tools like Cursor, GitHub Copilot, Claude, ChatGPT, or similar tools.
• Experience writing technical blogs, internal engineering guides, architecture documents, or enablement material that helps engineering teams adopt new practices.
• Experience leading technical standards or architecture forums across multiple engineering teams.

What Success Looks Like

• Adoption: Product teams across the company use the AI Enablement Platform as the default starting point for AI-powered features.
• Velocity: Teams can move from AI idea to production feature significantly faster because reusable APIs, SDKs, templates, evals, and guardrails are already available.
• Trust: AI systems are observable, measurable, debuggable, and safe to operate in production.
• Quality: Teams have clear evaluation practices, regression checks, and release gates for LLM-powered workflows.
• Reuse: Repeated AI patterns become shared capabilities instead of one-off implementations across product teams.
• Influence: The standard way of building AI systems becomes widely understood through architecture reviews, documentation, internal demos, and engineering best practices.
• AI-Native Engineering: Engineering teams use AI tools and platform capabilities to improve how software is designed, built, tested, reviewed, documented, and operated.

Compensation & What’s Offered

Compensation Range: 420,000 PLN – 450,000 PLN annually (Base compensation). In addition to base compensation, eligible employees may be offered other forms of incentive programs where applicable.

• Purpose-led company with a values-focused culture (Best Life, One Team, Growth Mindset).
• Open PTO Policy.
• Days of Disconnect — quarterly collective days off globally.
• Parental & Pawternity Leave.
• Fitness Perk — quarterly reimbursement for fitness activities.
• Partner Discounts — access to discounts with top technology partners.
• Medical / Dental / Vision coverage & Employee Assistance Program (EAP).
• Calm App +4 Subscription — for the employee and up to 4 dependents.