ML Architect

What you will do:

As an Architect, Machine Learning Engineer at Quantiphi, you will be responsible for designing and developing advanced machine learning models and algorithms to solve complex business problems. You will work on optimizing and deploying these models on AWS infrastructure, ensuring scalability and reliability.

Basic Qualifications (BQ):

• 8+ years of relevant hands-on technical experience implementing, and developing cloud ML solutions on AWS.
• Hands-on experience on AWS Machine Learning services. Proven experience using AWS Sagemaker leveraging different types of data sources, Training jobs, real-time and batch Inference, and Processing Jobs.
• Good Experience developing applications using LLMs with Langchain.
• Must have experience using GenAI frameworks such as vertexAI, OpenAI, AWS Bedrock.
• Must have Hands-on experience fine-tuning large language models( LLM) and Generative AI (GAI), specifically LLama2.
• Must have Hands-on experience working with (Retrieval Augmented Generation) RAG architecture and experience using vector indexing such as Opensearch, Elasticsearch.
• Strong familiarity with higher-level trends in LLMs and open-source platforms.
• Should have experience with Deep Learning Concepts. Transformers, BERT, Attention models
• Prompt Engineering: Engineer prompts and optimize few-shot techniques to enhance LLM's performance on specific tasks, e.g. personalized recommendations.
• Model Evaluation & Optimization: Evaluate LLM's zero-shot and few-shot capabilities, fine-tuning hyperparameters, ensuring task generalization, and exploring model interpretability for robust web app integration.
• Response Quality: Collaborate with ML and Integration engineers to leverage LLM's pre-trained potential, delivering contextually appropriate responses in a user-friendly web app.
• Implement and manage MLOps principles and best practices for Gen AI models
• Thorough understanding of NLP techniques for text representation and modeling
• Able to effectively design software architecture as required
• Experience with at least one of the workflow orchestration tools, Airflow, StepFunctions, SageMaker Pipelines, Kubeflow etc.Knowledge of a variety of machine learning techniques (Supervised/unsupervised etc.) (clustering, decision tree learning, artificial neural networks, etc.) and their real-world advantages/drawbacks
• Ability to create end to end solution architecture for model training, deployment and retraining using native AWS services such as Sagemaker, Lambda functions, etc.
• Ability to collaborate with cross-functional teams such as Developers, QA, Project Managers, and other stakeholders to understand their requirements and implement solutions.

What is in it for you:

• 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.
• Stay ahead of the curve by gaining hands-on experience with cutting-edge AI, ML, data, and cloud technologies while continuously upskilling.