2 days ago
Responsibilities
- Design and develop advanced machine learning models and algorithms for complex business problems.
- Optimize and deploy machine learning models on AWS infrastructure for scalability and reliability.
- Use Amazon SageMaker for data sources, training jobs, real-time and batch inference, and processing jobs.
- Develop LLM applications and use generative AI frameworks, including LangChain, Vertex AI, OpenAI, and AWS Bedrock.
- Fine-tune LLMs and generative AI models, specifically Llama 2.
- Implement RAG architectures using vector indexing technologies such as OpenSearch and Elasticsearch.
- Engineer prompts, optimize few-shot techniques, and evaluate zero-shot and few-shot model capabilities.
- Tune model hyperparameters, assess generalization and interpretability, and support robust web application integration.
- Implement and manage MLOps principles and practices for generative AI models.
- Design end-to-end AWS architectures for model training, deployment, and retraining.
- Collaborate with ML engineers, integration engineers, developers, QA, project managers, and other stakeholders.
Requirements
- At least 8 years of relevant hands-on technical experience implementing and developing cloud machine learning solutions on AWS.
- Hands-on experience with AWS machine learning services and Amazon SageMaker.
- Experience developing applications using LLMs with LangChain.
- Experience with generative AI frameworks such as Vertex AI, OpenAI, and AWS Bedrock.
- Hands-on experience fine-tuning large language models and generative AI models, specifically Llama 2.
- Experience with RAG architectures and vector indexing using OpenSearch or Elasticsearch.
- Strong familiarity with LLM trends and open-source platforms.
- Experience with deep learning concepts, Transformers, BERT, and attention models.
- Knowledge of NLP techniques, supervised and unsupervised learning, clustering, decision trees, and artificial neural networks.
- Experience with at least one workflow orchestration tool, such as Airflow, AWS Step Functions, SageMaker Pipelines, or Kubeflow.
- Ability to design software architecture and end-to-end solutions using native AWS services such as SageMaker and Lambda.
- Ability to collaborate effectively with cross-functional teams and stakeholders.
Benefits
- Full-time remote work from Canada.
- Opportunity to work at a fast-growing AI-first digital engineering company on large-scale projects.
- Collaboration with talented teams and Fortune 500 clients in a research-driven environment.
- Hands-on exposure to AI, machine learning, data, and cloud technologies with continuous upskilling opportunities.
Tech Stack
Categories
About Quantiphi
Quantiphi is an award-winning AI-first digital engineering company driven by the desire to reimagine and realize transformational opportunities at the heart of the business. Since its inception in 2013, Quantiphi has solved the toughest and most complex business problems by combining deep industry experience, disciplined cloud, and data-engineering practices, and cutting-edge artificial intelligence research to achieve accelerated and quantifiable business results. Learn more at www.quantiphi.com.