558 | Senior ML/Backend Engineer

Intetics Inc, a leading global technology company providing custom software application development, distributed professional teams, software product quality assessment, and “all-things-digital” solutions, is seeking a talented Senior ML/Backend Engineer to join our team.

About the Project: Innovative AI marketplace. From scratch, you'll be the first developer.

About the Role

We are seeking a highly skilled Senior ML/Backend Engineer to join our dynamic team at Intetics Inc. The ideal candidate has a robust background in Machine Learning, DevOps practices, and hands-on experience with Large Language Model (LLM)-based solutions. In this role, you will play a critical role in designing, developing, and deploying complex systems that leverage state-of-the-art ML techniques and language models. You'll work cross-functionally to integrate ML-driven functionalities seamlessly within our product stack, ensuring reliability, scalability, and operational efficiency.

Requirements

• Experience: 4+ years of engineering experience, with a focus on ML applications. 
• Hands-on experience with ML frameworks (TensorFlow, PyTorch) and knowledge of model training, evaluation, and deployment. 

• Proficient in creating CI/CD pipelines, infrastructure as code (e.g., Terraform, Ansible), and cloud platform management (AWS, GCP, Azure). 

• Demonstrated experience working with LLMs (e.g., GPT, Llama) for application in production environments. 

• Backend frameworks (Django or Flask or FastAPI).
• Experience with Code Assistants (Github Copilot, Codex, etc.). 

• Strong expertise in cloud services, containerization (Docker, Kubernetes), and microservices architecture. 
  
  Will be a Plus:
• Data Science Background: Experience with data analysis, statistical modeling, and familiarity with data science tools (e.g., Pandas, NumPy, Scikit-learn).
• Experience in NLP and conversational AI.
• Proficiency with frontend technologies (React, Angular, Vue)
• Familiarity with RESTful APIs and GraphQL.
• Understanding of data engineering principles and tools for handling large datasets.
• Exposure to A/B testing, experiment design, and metrics-driven development.