Sky Systems, Inc. (SkySys) company
Role: LLM Engineers
Position Type: Full-Time Contract (40hrs/week)
Contract Duration: 6 Months+
Work Hours: US Time
Work Schedule: 8 hours/day (Mon-Fri)
Location: 100% Remote (Candidates can work from anywhere in Canada)
Responsibilities:
Design, develop, and maintain scalable and efficient backend services for ML model deployment and inference using FastAPI for our AI driven product.
Implement various ML algorithms at scale e.g. time series forecasting, XGBoost, deep learning, NLP, etc. using GPUs clusters or Spark or Databricks or AWS Sagemaker or equivalent tools
Coordinating with development teams and data scientists to determine application requirements and integration points.
Understanding of fundamental design principles behind a scalable application and writing scalable code
Implement security best practices to safeguard sensitive data and ensure compliance with privacy regulations
Own and manage all phases of the software development lifecycle planning, design, implementation, deployment, and support.
Build reusable, high-quality code and libraries for future use which are high performant and can be used across multiple projects.
Work closely with data engineers and data scientists to integrate ML models into the production environment
Qualifications:
Bachelor's degree in Computer Science, Information Technology, or a related field (or equivalent work experience).
At least 3+ years of relevant experience as an ML Backend Engineer
Practical experience in applying AI/ML driven technology solutions. Experience in Generative AI with a strong understanding of deep learning techniques such as GPT, VAE, and GANs is preferred.
Experience in Fast API for API development, SQL/NoSQL databases and linux o.s.
Up to date with LLM research and developing new features/products using LLMs (e.g. using PaLM 2 or Llama 2)
Good experience with ML algorithms, Python, NLP , prior experience with backend design and web sockets
Knowledge of basic algorithms, object-oriented and functional design principles, and best-practice pattern
Experience with Cloud based AI/ML services such as AWS, GCP or Azure.
Understanding of ML/AI Pipeline & Development life cycle & tools, MLOps experience
Good knowledge of software engineering practices like version control (GIT), DevOps (Azure DevOps preferred) and Agile or Scrum.
Strong communication skills, with the ability to effectively convey complex technical concepts to a diverse audience.
