Quantiphi - Generative AI Architect - Data Modeling

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

Job Summary :

Role : Sr.Technical Lead ML.

Experience : 10 16 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.