Intetics
Senior ML/Bakcend Engineer
Job Description
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/Bakcend 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/Bakcend 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: 5+ 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 (Node.js, 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.
