Architect - Machine Learning - GenAI

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If working in an environment that encourages you to innovate and excel, not just in professional but personal life, interests you- you would enjoy your career with Quantiphi!

Role : Architect - Machine Learning (AWS)

Experience : 12 - 15 Years

Location : Bangalore

Must have skills:

• 12+ 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 AWS Bedrock, OpenAI.

• 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 optimizes 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.

• 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.

Good to have skills:

• Experience of working for customers/workloads in the Edtech domain with use cases. 

• Experience with software development