- Design, develop, and implement highly scalable backend services on AWS to support efficient training and inference of our LLM.
- Keep abreast of developments in AWS or other services to manage the LLM training and inference operations.
- Develop and optimise APIs to facilitate communication between the LLM and frontend applications for user interaction with functionalities from a variety of use cases.
- Design and implement data pipelines for efficiently moving training data to and from the LLM training environment on AWS (e.g., S3 Buckets, EFS storage).
- Integrate containerization technologies like Docker and container orchestration platforms like Amazon Elastic Kubernetes Service (EKS) for managing the LLM inference environment.
- Configure and implement robust security practices within your backend services to protect the LLM and user data at rest and in transit.
- Develop and implement logging and monitoring solutions (e.g., CloudWatch Logs, CloudWatch Metrics) to track the health and performance of the backend infrastructure and the LLM.
- Collaborate with ML and Front Eng Engineers to understand their specific requirements for training and inference, and translate those needs into efficient backend solutions.
- Collaborate with MLOps Engineers to automate the deployment and management of backend services and the LLM.
- Stay up-to-date on the latest advancements in AWS services relevant to backend development and LLM infrastructure.
- Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Linguistics, or a related field.
- Proven experience in NLP or related areas, with a strong portfolio of projects demonstrating expertise in data engineering and model training/finetuning.
- Solid understanding of machine learning algorithms, language modeling, and their applications.
- Proficiency in programming languages such as Python, and experience with NLP libraries (e.g., NLTK, spaCy, Hugging Face).
- Familiarity with popular vector database platforms like Pinecone, Faiss, or Milvus would be beneficial.
- Experience with cloud computing platforms and services, and the ability to deploy and manage large models in a cloud environment.
- Experience with data pipelines and data processing tools on AWS (e.g.AWS Lambda).
- Experience with Infrastructure as Code (IaC) tools like Terraform or CloudFormation.
- Strong problem-solving skills, with a creative and analytical approach to tackling complex challenges.
- Excellent communication and collaboration skills, with the ability to work effectively in a team environment.
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Description
Roles & ResponsibilitiesAbout the role
As an AI/ML Engineer at LTS, you will play a crucial role in developing and enhancing our AI-driven products and services. We're seeking a talented Software Engineer to join our team and be responsible for deploying, maintaining, and scaling the infrastructure that serves our custom LLMs for a variety of use cases.
Responsibilities:
Qualifications:
Job seekers who are interested can apply with their resumes, indicating their nationality, and current/last drawn and expected salaries.
Tell employers what skills you haveMachine Learning
Kubernetes
Pipelines
NLTK
CloudFormation
Artificial Intelligence
Data Engineering
PyTorch
Python
Containerization
Docker
Orchestration
S3
