Senior software engineer building AI evaluation pipelines, rubrics, and data - quality systems for a client. Remote contract role open across approved US regions and Canada with PST overlap.
About the role
- Machine Learning Architect designing and deploying AWS-based ML and GenAI solutions at Quantiphi. Building scalable LLM, RAG, and MLOps architectures for enterprise clients.
Responsibilities
- Design and develop advanced machine learning models and algorithms to solve complex business problems
- Optimize and deploy machine learning models on AWS infrastructure
- Ensure model scalability and reliability
- Engineer prompts and optimize few-shot techniques for specific tasks such as personalized recommendations
- Evaluate LLM zero-shot and few-shot capabilities
- Fine-tune hyperparameters and ensure task generalization
- Explore model interpretability for robust web application integration
- Collaborate with ML and integration engineers to deliver contextually appropriate responses in a user-friendly web application
- Implement and manage MLOps principles and best practices for Generative AI models
- Design software architecture and end-to-end solutions for model training, deployment, and retraining
- Collaborate with developers, QA, project managers, and other stakeholders to understand requirements and implement solutions
Requirements
- 8+ years of relevant hands-on technical experience implementing and developing cloud ML solutions on AWS
- Hands-on experience with AWS Machine Learning services
- Proven experience using AWS SageMaker with different data sources, training jobs, real-time and batch inference, and processing jobs
- Experience developing applications using LLMs with LangChain
- Experience using GenAI frameworks such as Vertex AI, OpenAI, and AWS Bedrock
- Hands-on experience fine-tuning large language models and Generative AI, specifically Llama 2
- Hands-on experience with Retrieval Augmented Generation (RAG) architecture
- Experience using vector indexing such as OpenSearch and Elasticsearch
- Strong familiarity with LLM trends and open-source platforms
- Experience with deep learning concepts, including Transformers, BERT, and attention models
- Prompt engineering and few-shot optimization
- LLM evaluation, hyperparameter fine-tuning, task generalization, and model interpretability
- MLOps principles and best practices for Generative AI models
- Thorough understanding of NLP techniques for text representation and modeling
- Ability to design software architecture
- Experience with at least one workflow orchestration tool: Airflow, Step Functions, SageMaker Pipelines, or Kubeflow
- Knowledge of supervised and unsupervised machine learning techniques, including clustering, decision tree learning, and artificial neural networks
- Ability to create end-to-end solution architecture for model training, deployment, and retraining using native AWS services such as SageMaker and Lambda
- Ability to collaborate with developers, QA, project managers, and other stakeholders
- Bachelor's degree or equivalent work experience is not stated
Benefits
- Join one of the world’s fastest-growing AI-first digital engineering companies and make a real impact at scale
- Lead and collaborate with a high-energy team of talented, driven individuals solving complex, meaningful challenges
- Work with Fortune 500 companies and disruptive innovators in a research-driven environment with 60+ patents
- Hands-on experience with cutting-edge AI, ML, data, and cloud technologies
- Continuous upskilling
- Work in a global, diverse culture built on transparency, diversity, integrity, learning and growth
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