Data Scientist

Brace Infotech Private Ltd; Hyderabad; On-site; INR 3,500,000 - 6,500,000; Full time

Job summary

You will shape ML pipelines, implement MLOps practices, and deploy scalable AI solutions on cloud platforms such as Azure or AWS, with emphasis on security and governance.

Qualifications

• Lead design, development and deployment of end-to-end data science and ML solutions.
• Strong hands-on experience in Python and libraries such as Pandas, NumPy, Scikit-learn, PyTorch, and TensorFlow.
• Design ML models for classification, regression, clustering, forecasting, recommendation, and NLP.
• Strong understanding of statistics, probability, hypothesis testing, experimentation, feature engineering, and model evaluation.
• Experience with Generative AI, LLMs, Prompt Engineering, RAG, embeddings, vector databases, and AI agents.
• Production-grade AI/ML solutions using Azure or AWS.
• Hands-on experience with Azure AI Foundry / Azure OpenAI / AWS SageMaker.
• Data pipelines and ML pipelines for training, validation, deployment, and monitoring.
• MLOps including versioning, experiment tracking, CI/CD, drift detection.
• Strong SQL and relational databases; familiarity with data warehouses/data lakes.
• REST APIs, microservices, Docker, and Kubernetes for deployment.
• Security, privacy, responsible AI, governance controls.

Responsibilities

• Lead the design, development and deployment of end-to-end data science and ML solutions.
• Oversee ML model development, evaluation, deployment and monitoring.
• Mentor Data Scientists and ML engineers and guide junior team members.
• Translate business requirements into scalable AI/ML solutions.
• Collaborate with cloud platforms to ensure secure and cost-efficient production deployments.

Skills

End-to-end data science

ML & DL algorithms

Statistical analysis

MLOps

AI governance & security

Business requirements translation

Leadership & mentoring

Python programming

Tools

Python

Pandas

NumPy

Scikit-learn

PyTorch

TensorFlow

Azure AI Foundry / Azure OpenAI

AWS SageMaker

Docker

Kubernetes

REST APIs

Git & CI/CD

SQL

Job description

• Lead the design, development, and deployment of end-to-end data science and machine learning solutions.
• Strong hands-on experience in Python and data science libraries such as Pandas, NumPy, Scikit-learn, PyTorch, and TensorFlow.
• Design and implement machine learning models for classification, regression, clustering, forecasting, recommendation, and NLP use cases.
• Strong understanding of statistics, probability, hypothesis testing, experimentation, feature engineering, and model evaluation.
• Experience with Generative AI, Large Language Models (LLMs), Prompt Engineering, RAG, embeddings, vector databases, and AI agents.
• Experience building and deploying production-grade AI/ML solutions using cloud platforms such as Azure or AWS.
• Hands-on experience with Azure AI Foundry / Azure OpenAI / Amazon SageMaker or equivalent AI/ML platforms.
• Experience designing data pipelines and ML pipelines for training, validation, deployment, and monitoring.
• Experience with MLOps, including model versioning, experiment tracking, CI/CD, model deployment, monitoring, and model drift detection.
• Strong experience working with SQL and relational databases and familiarity with data warehouses/data lakes.
• Experience with REST APIs, microservices, Docker, and Kubernetes for AI/ML application deployment.
• Implement AI/ML solutions with appropriate security, privacy, responsible AI, and governance controls.
• Optimize models for accuracy, scalability, latency, reliability, and cost.
• Lead technical discussions and mentor Data Scientists, ML Engineers, and junior team members.
• 8+ years of experience in Data Science / Machine Learning / AI.
• Strong programming experience in Python.
• Strong knowledge of Machine Learning and Deep Learning algorithms.
• Strong understanding of statistics and mathematical foundations of ML.
• Hands-on experience with Generative AI and LLM applications.
• Experience with RAG architectures, embeddings, vector databases, and semantic search.
• Experience with AI Agents / Agentic AI architectures.
• Experience with Azure AI Foundry, Azure OpenAI, AWS SageMaker, or equivalent platforms.
• Strong experience with ML model development, evaluation, deployment, and monitoring.
• Experience with MLOps and ML lifecycle management.
• Strong SQL and data manipulation skills.
• Experience with Git and CI/CD.
• Experience with Docker and Kubernetes is preferred.
• Experience with cloud services such as Azure / AWS.
• Strong understanding of AI security, responsible AI, data privacy, and AI governance.
• Ability to translate business requirements into scalable AI/ML solutions.