Machine Learning/Deep Learning Engineer

Position Overview:

Machine Learning/Deep Learning Engineer

This role will be instrumental in designing, training, and deploying advanced AI models across text, image, and audio domains, while also managing scalable cloud infrastructure and APIs.

Key Responsibilities:

Deep Learning & Neural Networks

• Design and implement deep learning models using

  TensorFlow, PyTorch, and transformer architectures.

• Fine-tune pre-trained models for domain-specific tasks involving text, image, or audio datasets.
• Optimize and deploy models on

  NVIDIA GPU hardware

  (e.g., A100, H100) for high-performance inference.
• Develop

  LLMOps context

  aware pipelines for chat applications using python frameworks.
• Collaborate with data scientists and product teams to integrate models into production systems.

API Development & Integration

• Develop and maintain

  RESTful

  and

  gRPC APIs

  for model serving and data access.
• Manage the full API lifecycle, including versioning, documentation, and security.
• Integrate APIs with internal and external applications using FastAPI or similar Python frameworks.

AWS Cloud Engineering

• Create and manage

  AWS Container Apps, Container Registries,

  and

  Docker images

  .
• Deploy and monitor AWS Web Apps for hosting AI services and dashboards.
• Automate CI/CD pipelines using GitHub Actions for seamless deployment and updates.

Required Qualifications

• 2+ years

  of experience in Python development with a focus on deep learning.
• Hands-on experience with

  transformers, Hugging Face, and custom model training.

• Proven track record of deploying models on

  GPU-based infrastructure

  .
• Strong understanding of API design principles and microservices architecture.
• Experience with

  AWS cloud services, Docker, and GitHub Actions

  .
• Excellent problem-solving skills and ability to work in a fast-paced, collaborative environment.

Preferred Qualifications

• Experience with

  multi-modal models

  or

  generative AI

  applications.
• Familiarity with

  MLOps tools

  and practices.
• Contributions to

  open-source AI

  projects or publications in relevant fields.