Lead Data Scientist

Evoke Technologies; Hyderabad; Hybrid; INR 1,800,000 - 3,500,000; Full time

Job summary

Evoke Technologies in Hyderabad invites an experienced ML/Data Science professional to design, build, and deploy end-to-end AI/ML solutions. The role emphasizes hands-on Python, modern ML libraries, and production-grade deployments on Azure/AWS.

You will architect data and ML pipelines, drive MLOps practices, and mentor junior team members while optimizing for performance, cost, and governance.

Qualifications

• Strong hands-on experience with Python and data science libraries (Pandas, NumPy, Scikit-learn).
• Expertise in building ML models across classification, regression, clustering, forecasting, recommender systems, and NLP.
• Solid foundation in statistics, probability, hypothesis testing, experimentation, feature engineering, and model evaluation.
• Experience with Generative AI, LLMs, prompt engineering, RAG, embeddings, vector databases, and AI agents.
• Proficiency deploying AI/ML solutions on Azure or AWS platforms.
• Hands-on experience with Azure AI Foundry / Azure OpenAI / SageMaker or equivalent.
• Design of data and ML pipelines for training, validation, deployment, and monitoring.
• Skills in MLOps: versioning, experiment tracking, CI/CD, deployment, monitoring, drift detection.
• Strong SQL and relational DB skills; familiarity with data warehouses/l Lakes.
• Experience with REST APIs, microservices, Docker, and Kubernetes for AI/ML deployment.
• Focus on security, privacy, responsible AI, and governance controls.
• Aim to optimize accuracy, scalability, latency, reliability, and cost.
• Ability to lead technical discussions and mentor junior team members.

Responsibilities

• Lead the design, development, and deployment of end-to-end data science and ML solutions.
• Strong hands-on experience in Python and data science libraries such as Pandas, NumPy, Scikit-learn, PyTorch, and TensorFlow.
• Design and implement ML 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, 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.

Skills

Python

Pandas

NumPy

Scikit-learn

PyTorch

TensorFlow

Generative AI

LLMs

Prompt Engineering

RAG

Embeddings

Vector Databases

AI Agents

Azure

AWS

SageMaker

Data Pipelines

MLOps

SQL

Data Warehouses/Data Lakes

REST APIs

Docker

Kubernetes

Security & Privacy

Governance

Model Deployment

CI/CD

Tools

Docker

Kubernetes

Azure AI Foundry

Azure OpenAI

Amazon SageMaker

REST APIs

Job description

Role & responsibilities

• 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.