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Senior Machine Learning Engineer

DecentralCode

5 - 10 years

Salem

Posted: 24/05/2026

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Job Description

Senior ML / Computer VisionEngineer



IntroductionDeCentralCode:


DeCentralCode is a global technology company with operations in Rotterdam, NL and Salem, TN. Focused on delivering modern technology solutions for real-world challenges.

Our expertise spans cutting-edge technologies including Machine Learning/Artificial Intelligence, Computer Vision, Distributed Ledger Technology, Blockchain, Internet of Things (IoT), Cloud-Native and Serverless Architecture, and Cybersecurity.

We collaborate with knowledge institutions, governments, and businesses to co-create and validate innovative solutions across multiple domains such as healthcare, supply chain, and energy transition. As an early-stage technology partner, we provide opportunities to build scalable products from the ground up using advanced and emerging technologies.


Location:

Salem, Tamil Nadu, India.

We are looking for:

We are developing advanced AI-driven solutions using Computer Vision, Deep Learning, and scalable Machine Learning technologies. The team is focused on building high-performance, production-ready AI systems with strong emphasis on model accuracy, reproducibility, optimisation, and scalable deployment architectures.

We are looking for a Senior ML / Computer Vision Engineer to lead the design, development, and optimisation of AI systems powering the products. This is a hands-on individual contributor role involving end-to-end ownership of machine learning workflows, including dataset strategy, evaluation protocols, model architecture design, experimentation, and deployment pipelines.

The role reports directly to the CTO/CPO, who has a strong ML background and serves as a technical sparring partner while expecting the candidate to drive key ML and Computer Vision decisions independently.


Responsibilities:

  • Own and manage end-to-end ML and Computer Vision pipelines from data processing to deployment.
  • Design, develop, and optimize scalable Deep Learning and AI models.
  • Define evaluation protocols, model architectures, and technical strategies.
  • Build reproducible MLOps workflows including experiment tracking and model versioning.
  • Analyze model performance and improve accuracy, scalability, and inference efficiency.
  • Collaborate with cross-functional teams to validate and enhance AI solutions.
  • Mentor junior ML engineers and provide technical guidance.
  • Work in an Agile environment with strong ownership of deliverables and timelines.
  • Identify technical risks, raise blockers proactively, and ensure high-quality execution.
  • Maintain technical documentation, engineering standards, and development best practices.

Necessary Skills:

Must Have:

  • 5+ years of production experience in ML, Deep Learning, and Computer Vision.
  • Strong expertise in Python, PyTorch, and modern AI/ML workflows.
  • Hands-on experience with CNN architectures like ResNet, EfficientNet, or similar.
  • Experience building and deploying production-grade Computer Vision systems.
  • Strong knowledge of data pipelines, preprocessing, augmentation, and validation.
  • Good understanding of statistical analysis, model evaluation, and optimization.
  • Experience with ordinal classification and multi-output learning techniques.
  • Self-driven experimentation and problem-solving mindset.
  • Proficiency in NumPy, pandas, scikit-learn, and related Python ML libraries.
  • Experience with Git, clean coding practices, and reproducible workflows.
  • Familiarity with AI-assisted development tools and Agile delivery practices.
  • Ability to build MVPs quickly and iterate scalable AI solutions efficiently.

Good to Have:

  • Experience in Medical Imaging, Healthcare AI, and Medical Image Analysis.
  • Hands-on experience working with small-data ML environments and multi-annotator datasets.
  • Knowledge of model optimization and on-device deployment using ONNX, TensorRT, or CoreML.
  • Familiarity with transfer learning, domain adaptation, and uncertainty quantification techniques.
  • Experience managing data annotation workflows and annotation tools.
  • Working knowledge of regulated software standards such as IEC 62304 and MDR.

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