DocPlanner

Senior Machine Learning Engineer

DocPlanner

full-time

Posted on:

Origin:  • 🇵🇱 Poland

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Job Level

Senior

Tech Stack

AirflowApacheAWSKubernetesPythonPyTorchTensorflow

About the role

  • Take technical leadership of ML initiatives, working closely with scientists, engineers, and product stakeholders to deliver AI-driven solutions that directly support strategic business objectives.
  • Design, deploy and iterate over ML services for diverse data types (e.g., audio, text), while proactively anticipating performance bottlenecks driving continuous improvements.
  • Brainstorm and design technical roadmaps in partnership with the AI Platform team, identifying and addressing platform and MLOps bottlenecks, and designing scalable GPU optimization strategies that balance performance, cost, and reliability.
  • Research, architect, and deploy LLM-powered information retrieval solutions (eg. RAG) to deliver accurate and scalable results in complex, multilingual product environments; champion industry-leading frameworks and evangelize their adoption across the organization.
  • Lead efforts to improve team effectiveness by evolving internal frameworks, optimizing workflows, and fostering a culture of operational excellence in collaboration with the AI Platform team.
  • Architect, deploy, and maintain high-throughput, reliable data pipelines to support training-set curation and data-annotation tooling.
  • Work alongside other machine learning professionals at various seniority levels and report directly into the Head of Machine Learning & Data Science.
  • Pair industry-leading ML rigor with pragmatic delivery, making smart trade-offs to ship value quickly and iterate fast.

Requirements

  • 5+ years of professional experience as an ML[Ops] Engineer in a fast-paced, product-driven tech environment.
  • Proven track record of delivering impactful ML initiatives in high-scale, cross-functional, and high-performance environments.
  • Demonstrated expertise in production-grade MLOps, leveraging, for example, orchestration with Kubernetes, model serving via FastAPI, NVIDIA Triton and KServe, Apache Airflow for data pipelines.
  • Good understanding and proficiency in deep learning frameworks such as PyTorch or TensorFlow.
  • Proven ability to integrate, deploy, and optimize large language models in production-grade industry environments, ensuring scalability and robust performance.
  • Knowledgeable in prompt engineering, basis of agent‐based workflows, and the generation and manipulation of embeddings.
  • Problem-solving mindset and adaptability in dynamic environments with a focus on delivering business value to end customers.
  • Strong collaboration and communication skills, with a track record of influencing cross-functional stakeholders and aligning diverse teams around shared goals.
  • Proven ability to manage timelines, prioritize tasks, and deliver results under tight deadlines.
  • Experienced in mentoring and guiding other engineers, fostering technical growth and promoting a high-performance team culture.
  • Curiosity and eagerness to collaborate with cross-functional teams (e.g., product, marketing, engineering)