Machine Learning Engineering Lead

About Client :

Our Client is a global IT services company headquartered in Southborough, Massachusetts, USA. Founded in 1996, with a revenue of $1.8B, with 35,000+ associates worldwide, specializes in digital engineering, and IT services company helping clients modernize their technology infrastructure, adopt cloud and AI solutions, and accelerate innovation. It partners with major firms in banking, healthcare, telecom, and media.

Our Client is known for combining deep industry expertise with agile development practices, enabling scalable and cost-effective digital transformation. The company operates in over 50 locations across more than 25 countries, has delivery centers in Asia, Europe, and North America and is backed by Baring Private Equity Asia.AI / ML -Engineering

Job Title : AI / ML -Engineer -Face-to-Face Interview

Experience Level : 8-14 Years AI / ML -Engineering

Job Location : Hyderabad

Budget : 1,50,000 Per Month

Job Type : Contract

Work Mode : Hybri

Notice Period : Immediate Joiners

Client : CMMI Level 5

immdemity joinner

Job Description :

We are seeking a highly motivated and experienced ML Manager / Lead Data Scientist to join our

growing ML / GenAI team.

You will play a key role in designing, developing and productionalizing ML / GenAI applications by

evaluating models, training and / or fine tuning them. As a lead, you require a deep

understanding of both machine learning algorithms and software architecture. You need to take

ownership of projects from conception to deployment, collaborating with engineers and

stakeholders to ensure successful project delivery.

What we're looking for :

• At least 5 years of experience in designing & building ML / AI applications for customer

and deploying them into production

performant applications for customers.

members)

AI.

prompt development, including data cleaning, transformation, and augmentation.

improve LLM performance.

Generative AI solutions. This includes designing RAG (Retrieval-Augmented Generation)

pipelines, agentic workflows, and model fine-tuning strategies.

platform involving data warehouses, machine learning platforms, dashboards or CRM

tools.

exploratory data analysis, dealing outliers, handling imbalances, analyzing data

distributions (univariate, bivariate, multivariate), transforming numerical and categorical

data into features, feature selection, model selection, model training and deployment.

includes building core components for prompt engineering, vector search, data

processing, model evaluation, and inference serving.

environments for real life applications

learning techniques.

TensorFlow, PyTorch, Scikit-learn, LangChain).

services.

Good to Have

Developer certifications or equivalent.

Azure.