Data Scientist GEN AI

1) Data Scientist – Generative AI & ML

Job Description

Function: Data Science and Analysis → Data Science / Machine Learning

Below 4 skill is must have

Generative AI

Python

LLMs

MLOps

We are looking for an experienced and visionary Generative AI Architect with 12-15 years of

experience in AI/ML, including hands-on work with LLMs (Large Language Models) and Generative

AI solutionsIn this strategic technical leadership role, you will be responsible for designing and

overseeing the development of advanced GenAI platforms and solutions that transform business

operations and customer experiences.

As the GenAI Architect, you will work closely with data scientists, ML engineers, product teams, and

stakeholders to conceptualize, prototype, and scale generative AI use cases across the

organization or client engagements.

Responsibilities:

Lead the design and development of scalable GenAI solutions leveraging LLMs, diffusion models,

and multimodal architectures.

Architect end-to-end pipelines involving prompt engineering, vector databases, retrieval-augmented

generation (RAG), and LLM fine-tuning.

Select and integrate foundational models (e. g., GPT, Claude, LLaMA, Mistralbased on business

needs and technical constraints.

Define GenAI architecture blueprints, best practices, and reusable components for rapid

development and experimentation.

Guide teams on model evaluation, inference optimization, and cost-effective scaling strategies.

Stay current on the rapidly evolving GenAI landscape and assess emerging tools, APIs, and

frameworks.

Work with product owners, business leaders, and data teams to identify high-impact GenAI use

cases across domains like customer support, content generation, document understanding, and

code generation.

Support PoCs, pilots, and production deployments of GenAI models in secure, compliant

environments.

Collaborate with MLOps and cloud teams to enable continuous delivery, monitoring, and

governance of GenAI systems.

Requirements:

Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Data Science, or related

technical field. A PhD is a plus.

12-15 years in AI/ML and software engineering, with 3+ years focused on Generative AI and

LLM-based architectures.

Core Skills:

Deep expertise in machine learning, natural language processing (NLP), and deep learning

architectures.

Hands-on experience with LLMs, transformers, fine-tuning techniques (LoRA, PEFT), and prompt

engineering.

Proficient in Python, with libraries/frameworks such as Hugging Face Transformers, LangChain,

OpenAI API, PyTorch, TensorFlow.

Experience with vector databases (e. g., Pinecone, FAISS, Weaviate) and RAG pipelines.

Strong understanding of cloud-native AI architectures (AWS/GCP/Azure), containerization

(Docker/Kubernetes), and API integration.

Architectural & Leadership Skills:

Proven ability to design and deliver scalable, secure, and efficient GenAI systems.

Strong communication skills for cross-functional collaboration and stakeholder engagement.

Ability to mentor engineering teams and drive innovation across the AI/ML ecosystem.

Nice-to-Have:

Experience with multimodal models (text + image/audio/video).

Knowledge of AI governance, ethical AI, and compliance frameworks.

Familiarity with MLOps practices for GenAI, including model versioning, drift detection, and

performance monitoring.

2) Data Scientist – Classical ML

Job description

We are looking for a Senior Data Scientist who is passionate about solving complex business

problems using data. The ideal candidate will have strong hands-on experience in Python, SQL,

and advanced machine learning algorithms, along with domain expertise in forecasting, pricing

optimization, or inventory planning. You will work closely with cross-functional teams including

product, engineering, and business stakeholders to drive data-driven strategies.

Classical Ml - Linear reg, random forest, time series, decission trees, xg boost, logistic reg

Key Responsibilities:

Design, build, and deploy predictive and optimization models for use cases such as demand

forecasting, dynamic pricing, or inventory optimization.

Translate business problems into analytical frameworks and provide actionable insights.

Build and maintain scalable data pipelines and model workflows using Python and SQL.

Collaborate with data engineers to ensure model integration into production systems.

Present findings and recommendations to leadership using data visualizations and storytelling.

Stay current with the latest techniques in machine learning, statistics, and operations research.

Lead and mentor junior data scientists and analysts when required.

Required Skills and Experience:

5+ years of experience in a Data Science or Analytics role with a strong business impact.

Strong programming skills in Python (pandas, scikit-learn, statsmodels, etc.).

Proficiency in SQL for data extraction, transformation, and manipulation.

Deep understanding of machine learning algorithms (regression, classification, clustering, time

series forecasting).

Experience with one or more of the following domains:

Demand Forecasting

Price Optimization

Inventory Optimization / Supply Chain Analytics

Strong problem-solving and quantitative skills.

Experience working with large datasets and distributed computing tools (e.g., Spark, Hadoop) is a

plus.

Familiarity with data visualization tools (e.g., Tableau, Power BI, matplotlib, seaborn).