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 processing, training jobs, real-time inference, batch inference, and processing jobs.
- Develop LLM applications using LangChain and generative AI frameworks including Vertex AI, OpenAI, and AWS Bedrock.
- Fine-tune LLMs and generative AI models, including Llama 2.
- Implement RAG architectures and vector indexing with OpenSearch or Elasticsearch.
- Engineer prompts, optimize few-shot techniques, evaluate model performance, tune hyperparameters, and assess model interpretability.
- Implement and manage MLOps practices for generative AI models.
- Design end-to-end AWS architectures for model training, deployment, and retraining using services such as SageMaker and Lambda.
- Collaborate with developers, QA, project managers, ML engineers, integration engineers, and other stakeholders to understand requirements and implement solutions.
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, especially Amazon SageMaker and its data, training, inference, and processing capabilities.
- Experience developing applications with LLMs and LangChain.
- Experience with generative AI frameworks such as Vertex AI, OpenAI, and AWS Bedrock.
- Hands-on experience fine-tuning LLMs and generative AI models, specifically Llama 2.
- Hands-on 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 including Transformers, BERT, and attention models.
- Thorough understanding of NLP techniques for text representation and modeling.
- Experience with workflow orchestration tools such as Airflow, AWS Step Functions, SageMaker Pipelines, or Kubeflow.
- Knowledge of supervised and unsupervised machine learning techniques, including clustering, decision trees, and artificial neural networks.
- Ability to design software architectures and collaborate effectively with cross-functional stakeholders.
Benefits
- Full-time remote position based in the USA.
- Opportunity to work with Fortune 500 companies and disruptive innovators on large-scale AI transformation projects.
- Hands-on exposure to AI, machine learning, data, cloud, and generative AI technologies with ongoing upskilling opportunities.
- Opportunity to collaborate with a high-energy team in a research-driven environment with more than 60 patents.
- Opportunity to contribute to an AI-first digital engineering company and work on complex, meaningful challenges.
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.