Machine learning Engineer

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.