Senior Data Scientist (End-to-End ML/DL/NLP)
Location: On-Site (Gurugram)Experience: 5-6 YearsIndustry: AI/ML, Deep Learning, NLP, Cloud
ComputingEmployment Type: Full-time
About the Role
We are seeking a Senior Data Scientist with strong expertise in Machine Learning, Deep
Learning, and NLP to lead end-to-end AI/ML projects in a fast-paced startup environment. The
ideal candidate will have hands-on experience in building and deploying scalable ML solutions on
cloud, managing AI/ML pipelines, and ensuring production-grade reliability.
This role requires deep technical proficiency, problem-solving abilities, and experience in handling ML
infrastructure, cloud computing, and MLOps. The candidate must be comfortable working in a
startup environment, taking ownership of projects, and delivering impactful AI solutions.
Key Responsibilities
Design, develop, and deploy ML, Deep Learning, and NLP models from concept to production.
Build and manage end-to-end ML pipelines, including data preprocessing, model training,
optimization, and deployment.
Implement scalable and efficient ML models using Cloud, Docker, and cloud-native
solutions.
Ensure seamless integration of AI/ML models into production systems with MLOps best
practices.
Work on real-time and batch data processing for AI-driven applications.
Manage and optimize Cloud infrastructure, ensuring cost-effective and scalable AI
deployments.
Collaborate with engineering and product teams to align AI solutions with business goals.
Handle model monitoring, performance tracking, and drift detection to ensure accuracy over
time.
Required Skills & Experience
5-6 years of experience in AI/ML, Deep Learning, and NLP, with a strong focus on end-to-end
ML deployments.
Prior experience working in a product based environment, with a track record of delivering AI
driven products at scale.
Proficiency in Python (TensorFlow, PyTorch, Scikit-learn, FastAPI, Pandas, NumPy).
Expertise in Cloud (Cloud Data Factory) and experience.
Strong background in MLOps, CI/CD, and model deployment using MLflow, Airflow, Docker.
Experience in big data processing with tools like Kafka, Spark
Ability to translate business problems into AI/ML solutions and optimize models for
production.
Solid understanding of feature engineering, model tuning, and production monitoring.
Preferred SkillsExperience with Large Language Models (LLMs) such as GPT, BERT, T5, Llama, or Falcon.
Knowledge of vector databases.
Experience in Edge AI, TinyML, or Reinforcement Learning.
Background in recommendation systems, anomaly detection, Fraud Detection or predictive
modeling