While technology is the heart of our business, a global and diverse culture is the heart of our success. We love our people and we take pride in catering them to a culture built on transparency, diversity, integrity, learning and growth.
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
If you like wild growth and working with happy, enthusiastic over-achievers, you'll enjoy your career with us!
Skills Required
- 12+ years hands-on experience implementing and developing cloud ML solutions on AWS
- Hands-on experience with AWS machine learning services, specifically AWS SageMaker (training, processing, real-time and batch inference)
- Experience developing applications using LLMs with LangChain
- Experience with GenAI frameworks such as AWS Bedrock and OpenAI
- Hands-on experience fine-tuning large language models and generative AI, specifically Llama2
- Experience implementing RAG architectures and vector indexing (OpenSearch, Elasticsearch)
- Strong familiarity with LLM trends and open-source LLM platforms
- Deep learning knowledge (Transformers, BERT, attention models)
- Prompt engineering and few-shot technique optimization
- Model evaluation, hyperparameter tuning, optimization and interpretability for web integration
- Thorough understanding of NLP techniques for text representation and modeling
- Ability to design software and solution architecture for model training, deployment and retraining using AWS (SageMaker, Lambda, etc.)
- Experience with workflow orchestration tools (Airflow, AWS Step Functions, SageMaker Pipelines, Kubeflow)
- Knowledge of a variety of machine learning techniques (supervised, unsupervised, clustering, decision trees, neural networks)
- Ability to collaborate with cross-functional teams (Developers, QA, PMs, stakeholders)
- Experience working on EdTech customer workloads/use cases
- Experience with software development
Quantiphi Compensation & Benefits Highlights
The following summarizes recurring compensation and benefits themes identified from responses generated by popular LLMs to common candidate questions about Quantiphi and has not been reviewed or approved by Quantiphi.
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Flexible Benefits — Hybrid and work-from-home options are commonly available and perceived as meaningful perks that increase overall package value. Flexibility by team and role often enhances day-to-day experience even when cash pay is not top-tier.
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Healthcare Strength — U.S. materials indicate medical coverage that includes dental and vision, and employee accounts align with having these plans in place. The presence of core health benefits contributes to a baseline of security across key locations.
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Parental & Family Support — Paid parental leave is available in the U.S., with examples citing generous leave lengths. Family-focused policies appear alongside other flexibility features.
Quantiphi Insights
What We Do
Quantiphi is an award-winning AI-first digital engineering company driven by the desire to solve transformational problems at the heart of business. Quantiphi solves the toughest and complex business problems by combining deep industry experience, disciplined cloud, and data-engineering practices, and cutting-edge artificial intelligence research to achieve quantifiable business impact at unprecedented speed.
