Bjak

LLM Application Engineer

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MidZdalnie·WarszawaFull-time

#411413·Dodano dwa miesiące temu·4

Źródło: Bjak

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Tech Stack / Keywords

LLMAIDatabasesBackendPython OpenAI PyTorch Generative AI

Firma i stanowisko

There are over 5 billion users using basic applications today such email, notes, tasks that are not AI-native. Our mission is to build a proactive smart assistant for everyday users to bring intelligence to conversations, errands, organising and workflows, with minimal prompting.

Our product focuses on achieving high reliability for long-running workflows, persistent context, and real-world task completion. The system must handle multi-step reasoning, interact with external tools, and remain reliable despite non-deterministic model behavior. Our objective is to help users complete tasks daily enjoyable with over ~90%* reduced time.

Wymagania

Strong software engineering fundamentals with experience building AI-powered applications
Hands-on experience with LLMs, generative AI, or agent-based systems
Experience designing prompts, workflows, evaluations, or AI behaviour
Ability to write clean, production-quality code
Comfortable working across abstraction layers (model → system → product)
Strong problem-solving skills in ambiguous, fast-moving environments
Bias toward shipping, iteration, and continuous improvement

Obowiązki

Build and ship LLM-powered applications and AI agent workflows
Design systems for reasoning, planning, memory, tool use, and multi-step execution
Build reliable orchestration pipelines that convert probabilistic model outputs into predictable, observable, and safe actions
Integrate LLMs with APIs, databases, search, internal services, and external tools
Develop prompting, context engineering, structured outputs, tool-calling, and other techniques to improve model behaviour
Build evaluation frameworks and datasets to measure AI quality, reliability, and regressions
Debug AI systems across the entire stack—from model behaviour and prompts to orchestration, backend services, and product UX
Optimise AI systems for quality, latency, and cost
Work closely with product and engineering teams to convert ambiguous product problems into working AI solutions
Establish production practices for observability, tracing, experimentation, evaluation, and continuous improvement

Bjak

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