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Principal AI/ML Engineer

symphony.is · Colombie

Senior 🇬🇧 English
Python PyTorch TensorFlow Transformers Diffusion models LoRA QLoRA instruction tuning CI/CD for ML model serving

Job description

About the role

Symphony is seeking a Principal AI/ML Engineer to lead cutting‑edge artificial intelligence initiatives for global enterprise clients. This hands‑on technical leadership position involves shaping AI solutions, building proof‑of‑concepts, and delivering production‑ready machine‑learning systems across domains such as audio/video processing, natural language processing, computer vision, and complex business workflows.

Key responsibilities

  • Translate business challenges into AI/ML solutions and lead discovery workshops.
  • Design, develop, and present proof‑of‑concepts to validate feasibility and value.
  • Contribute technical input to proposals, scoping, and estimations.
  • Develop models in NLP, computer vision, audio, and generative AI, including fine‑tuning and parameter‑efficient methods (LoRA, QLoRA, instruction tuning).
  • Architect scalable deployments (APIs, batch, real‑time) and implement CI/CD pipelines for training, evaluation, versioning, and deployment.
  • Define monitoring, drift detection, and optimization standards.
  • Collaborate with data engineers on pipelines and feature engineering.
  • Set coding standards, lead design reviews, and mentor engineers.
  • Communicate technical decisions to engineering and executive audiences.

Required profile

  • Bachelor’s degree in Computer Science, IT, or related field, or equivalent experience.
  • 10+ years of software engineering or ML experience, with at least 5 years focused on production ML systems.
  • Strong background in consulting or client‑facing technical leadership.
  • Proven track record delivering ML solutions across multiple industries.

Required skills

  • Expertise in Python and solid software engineering fundamentals.
  • Deep experience with PyTorch or TensorFlow.
  • Hands‑on work with modern architectures such as Transformers and Diffusion models.
  • Experience fine‑tuning large language models (e.g., Llama, Mistral) and using parameter‑efficient techniques.
  • Model serving, containerisation, and CI/CD for machine learning.

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Published 3 months ago

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symphony.is

Colombie