Machine Learning Engineer (Freelancer)

2 - 5 years

2 - 5 Lacs

Posted:1 week ago| Platform: Foundit logo

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Skills Required

Work Mode

Remote

Job Type

Full Time

Job Description

Deccan AI is building an exclusive community of top-tier Machine Learning Engineers who excel at building, deploying, and optimizing AI models. If you have proven experience in this cutting-edge field, this is your chance to collaborate with industry leaders and tackle some of the most complex AI challenges.

What's in it for you

  • Above-market pay for your expertise.
  • Flexible work arrangements: This role is contract-based with project timelines ranging from 2-6 months, or you can work as a freelancer.
  • Become part of an elite community of professionals dedicated to solving advanced AI problems.
  • Work location flexibility:
  • Remote (most likely)
  • Onsite at a client's location
  • Deccan AI's office in Hyderabad or Bangalore

Responsibilities:

  • Design, optimize, and deploy machine learning models.
  • Implement robust feature engineering and scaling pipelines.
  • Utilize deep learning frameworks such as TensorFlow and PyTorch.
  • Effectively manage models in production using tools like Docker and Kubernetes.
  • Automate workflows, ensuring efficient model versioning, logging, and real-time monitoring.
  • Maintain strict compliance with security protocols and regulations.
  • Work with large-scale data, develop sophisticated feature stores, and implement CI/CD pipelines for continuous model retraining and performance tracking.

Required Skills:

  • Proficiency in machine learning, deep learning, and data engineering (e.g., Spark, Kafka).
  • Expertise in MLOps principles and automation tools like Docker, Kubernetes, Kubeflow, MLflow, and TFX.
  • Strong command of cloud platforms (AWS, GCP, Azure).
  • In-depth knowledge of model deployment, monitoring, security, and compliance.
  • Commitment to and understanding of responsible AI practices.

Nice to Have:

  • Experience with A/B testing, Bayesian optimization, and hyperparameter tuning.
  • Familiarity with multi-cloud ML deployments.
  • Understanding of generative AI technologies (e.g., LLM fine-tuning, FAISS).

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