Key Responsibilities
Design, develop, and deploy Machine Learning and AI models for business applications.
Build scalable AI/ML pipelines for training, testing, deployment, and monitoring.
Perform data collection, preprocessing, feature engineering, and model optimization.
Develop predictive, classification, recommendation, and forecasting models.
Implement Deep Learning models using industry-standard frameworks.
Build Generative AI solutions leveraging Large Language Models (LLMs).
Fine-tune foundation models and optimize inference performance.
Collaborate with business teams to translate requirements into AI-driven solutions.
Develop APIs and integrate AI models into enterprise applications.
Establish MLOps best practices for model lifecycle management.
Monitor model performance, drift, and retraining requirements.
Create technical documentation, architecture diagrams, and deployment guides.
Stay updated with emerging AI technologies and industry trends.
Required Skills
Artificial Intelligence & Machine Learning
Machine Learning Algorithms
Supervised & Unsupervised Learning
Reinforcement Learning
Deep Learning
Generative AI
Natural Language Processing (NLP)
Computer Vision
Recommendation Systems
Time Series Forecasting
Programming
Python
SQL
PySpark
R (Preferred)
AI/ML Frameworks
TensorFlow
PyTorch
Keras
Scikit-learn
XGBoost
LightGBM
Generative AI
LLMs
Prompt Engineering
RAG (Retrieval-Augmented Generation)
Vector Databases
LangChain
Semantic Search
AI Agents
Data Engineering
ETL/ELT Pipelines
Data Warehousing
Data Lakes
Feature Engineering
Data Validation