Associate Architect – Machine Learning

Quantiphi

Associate Architect focused on AWS Machine Learning solutions at Quantiphi. Collaborating on ML model development and implementation in a diverse, innovative culture.

Posted 6/4/2026full-timeBengaluru • 🇮🇳 IndiaJuniorMid-LevelWebsite

Tech Stack

Tools & technologies

AirflowAWSCloudElasticSearch

About the role

Key responsibilities & impact

Work as an Associate Architect - Machine Learning (AWS)
Collaborate with cross-functional teams to deliver cloud ML solutions
Implement and develop machine learning models on AWS
Optimize and evaluate machine learning workflows and models
Design and maintain software architecture for cloud-based applications

Requirements

What you’ll need

7 - 13 years of experience
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
Experience using GenAI frameworks such as AWS Bedrock, OpenAI
Hands-on experience fine-tuning large language models( LLM) and Generative AI (GAI), specifically LLama2
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
Experience with Deep Learning Concepts
Transformers, BERT, Attention models
Experience with 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
Ability 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.

Benefits

Comp & perks

A culture built on transparency, diversity, integrity, learning and growth
Ample opportunities to learn, grow and interact with colleagues from varied experience and backgrounds around the globe.

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills & Tools

machine learningAWSAWS SagemakerLLMsLangchainGenerative AILLama2RAG architecturevector indexingDeep Learning

Soft Skills

collaborationdesignoptimizationevaluationcommunication