Technical Architect - Machine Learning

Quantiphi

7d•Remote

About The Position

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.

Requirements

7+ 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.
Natural Language Processing experience.
Experience with Deep Learning Concepts - Transformers, BERT, Attention models.
Strong programming skills in Python and experience with NLP libraries such as Hugging Face Transformers, SpaCy, NLTK, or Stanford NLP.
Strong understanding of NLP concepts: tokenization, embeddings, syntactic and semantic parsing, named entity recognition, and coreference.
Experience with Agentic AI Design and implementation using frameworks such as LangChain or Amazon Bedrock Agents.
Hands-on experience fine-tuning large language models (LLM) and Generative AI (GAI), specifically LLama2.
Familiarity with LLM tool use, prompt templating, vector stores (e.g., Opensearch, Pinecone, Elasticsearch, Bedrock KB), and context management.
Experience with Model Evaluation & Optimization for LLMs.
Experience with at least one of the workflow orchestration tools, Airflow, StepFunctions, SageMaker Pipelines, Kubeflow etc.
Experience implementing secure, scalable APIs and integrating with 3rd-party data sources and tools.
Ability to collaborate with cross-functional teams such as Developers, QA, Project Managers, and other stakeholders to understand their requirements and implement solutions.

Nice To Haves

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

Responsibilities

Design and develop advanced machine learning models and algorithms to solve complex business problems.
Optimize and deploy machine learning models on AWS infrastructure, ensuring scalability and reliability.
Design and implement agentic AI architectures using frameworks such as LangChain or Amazon Bedrock Agents, enabling autonomous task planning, decision-making, and multi-step reasoning.
Architect and deploy scalable AI solutions on AWS, leveraging services like Lambda, Bedrock, Step Functions, S3, API Gateway, and SageMaker.
Develop and maintain Model Context Protocol (MCP) implementations to manage state, context windows, memory, and prompt orchestration across distributed agent systems.
Integrate agentic workflows with LLMs (Titan, Nova, Cohere, Claude, etc.) via Bedrock, ensuring high-availability and multi-model support.
Evaluate LLM's zero-shot and few-shot capabilities, fine-tuning hyperparameters, ensuring task generalization, and exploring model interpretability for robust web app integration.
Implement secure, scalable APIs and integrate with 3rd-party data sources and tools.
Collaborate with cross-functional teams such as Developers, QA, Project Managers, and other stakeholders to understand their requirements and implement solutions.