Gen AI Architect

Quantiphi is an award-winning AI-first digital engineering company driven by the desire to

reimagine & realize transformational opportunities at the heart of the business. We are

passionate about our customers & obsessed with problem-solving to make products

smarter, customer experiences frictionless, processes autonomous & businesses safer.

We put together a wide array of solutions that help businesses build AI products, find &

retain high-value customers, improve operating efficiency & reduce risk across several

industries including but not limited to Healthcare, Insurance, Media, Retail, Manufacturing, &

Consumer Products & are in partnership with Google Cloud, AWS, NVIDIA, Looker,

Snowflake, SAP & Tensorflow.

For more details visit:

Job Summary

Role : Sr. Technical Lead - ML

Experience : 12- 18 Years

We are seeking a highly experienced ML Architect/Applied AI Researcher with a

specialization in deep learning and generative models to join our dynamic team. In this

senior role, you will evangelize the latest AI research and guide the strategic architectural

direction of our AI modeling and implementation efforts, including the evaluation and

fine-tuning of large language models (LLMs), and the evolving automation based on

customer needs.

The ideal candidate has a robust understanding of the recent developments in

reinforcement learning with human feedback (RLHF) and can adeptly apply this knowledge

in a practical setting. They will be a self-motivated, entrepreneurial, and demonstrated

team-player, as well as an early thought leader and hands-on implementer along with the

teams and developing best practices and recommendations around tools/technologies for

ML life-cycle capabilities such as Data collection, Data preparation, Feature Engineering,

Model Management, MLOps, Model Deployment approaches and Model monitoring and

tuning.

Responsibilities

Defining, designing and delivering ML architecture patterns operable in

native and hybrid cloud architectures.

Research, analyze, recommend and select technical approaches to address

challenging development and data integration problems related to ML

Model training and deployment in Enterprise Applications.

Perform research activities to identify emerging technologies and trends

that may affect the Data Science/ ML life-cycle management in enterprise

application portfolio.

Ability to multitask and work on multiple engagements related to different

domains.

Work in a highly collaborative and fast paced environment by interacting

with the stakeholders and various IT teams within the company to facilitate

the design and development of ML/AI solutions.

Responsible for the successful delivery of all allocated projects with respect

to schedule, quality, and customer satisfaction.

Work with the pre-sales team on RFP, RFIs and help them solutioning for

different AI/ML use cases.

.Evaluate latest technologies, decide technical feasibility, and drive solution

implementations.

Follow Agile standards and methodologies in all phases of the project and

ensure excellence in delivery to customers.

Refine coding standards, software development guidelines, and best

practices within the organization, and ensure adherence to those.

Mentor other architects and young talent within the organization, define

and track their growth parameters.

What is Required

Strong interpersonal and written skills with clear and precise

communication.

Experience working in an Agile and competitive environment.

Technical leadership experience handling large teams.

Stakeholder interaction experience both within the organization and outside

with clients.

Strong analytical and quantitative skill set with proven experience solving

business problems across domains.

Very good with EDA, Hypothesis Testing, Feature Engineering.

Hands-on with Python/R programming and ML/Viz. libraries/frameworks like

Scikit-Learn, Pandas, Matplotlib, Seaborn, D3.js,Tensorflow, Pytorch, Keras.

Experience with ML algorithms such as Regression and Classification

(Decision-trees, Random Forests, SVM, ANNs), Clustering(k-means, DBSCAN),

Dimension Reduction (PCA, SVD), Ensemble techniques (XGBoost, CatBoost,

LightGBM).

Basic image enhancement techniques such contrast enhancement,

blurring, histogram equalization, etc using OpenCV.

Experience with DL/CV techniques like CNNs, Faster RCNN, Mask RCNN,

YOLO, SSD, Detectron2 for various use cases related toimage such as Image

Classification, Object Detection, Image Segmentation, etc.

Traditional NLP - Bag of words, tf-idf, Stemming, Lemmatization,

Tokenization, POS tagging, Coreference Resolution,Dependency and

Constituency Parsing, Named Entity Recognition.

NLP: NLU vs NLG, Vector Space modeling and text representation

techniques in NLP, Knowledge/experience using RNNs,LSTMS, Sequence

modeling and Attention mechanism, Transformers, BERT, GPT and their

SOTA variants, Sequence modeling,Attention modeling, BERT,

Transformers.Using the above-mentioned techniques for Text classification,

Sentiment Analysis,Semantic similarity, Entity Extraction, Document

summarization, NLI, Question-Answering, Machine Translation, etc

Forecasting modeling experience both on univariate and multivariate data

using algorithms like Linear Regression, Neural Networks, Exponential

Smoothing, Holts Winters, ARIMA, SARIMA, LSTM.

Identify appropriate objective functions, regularization techniques,

performance metrics based on the use case and should be able to perform

cross-validation, hyperparameter tuning, and error analysis.

Proven experience building and deploying AI/ML solutions in production

using open source or cloud tools such as MLflow, Kubeflow, TFX, Feature

Store, etc.

Hands-on AI/ML experience with any one cloud platform (GCP, Azure, AWS)

either using the modeling options (VertexAI, SageMaker, Azure ML) or

leveraging the APIs (Speech-to-text, Text-to-speech, Translation, Vision, Text

Extraction).

Nice to have skills:

In-depth experience in AI/ML and Data analytics services offered.

Demonstrated experience developing best practices and recommendations

around tools/technologies for ML life-cycle capabilities such as Data

collection, Data preparation, Feature Engineering, Model Management,

MLOps, Model Deployment approaches and Model monitoring and tuning.

Whats in it for you?

1. The experience of working in a category defining high growth startups in

the transformational AI, Decision Science and Big Data Domain.

2. The opportunity of getting on boarded to the phenomenal growth journey

and helping the customers take the next big leap in digital transformation

3. The opportunity to work with a diverse, lively and proactive group of techies

who are constantly raising the bar on the art of translating mounds of data

into tangible business value for clients.

4. Flexible working options available to foster productivity and work/life

balance.