Gen AI Architect

Location: Bengaluru

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, Cat Boost,

Light

GBM).

● Basic image enhancement…