AI/ML Engineer - S

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

MLOps & Deployment

• MLflow
• Kubeflow
• Docker
• Kubernetes
• CI/CD Pipelines
• Model Monitoring
• A/B Testing

Cloud Platforms

• AWS SageMaker
• Azure AI Services
• Microsoft Fabric
• Google Vertex AI
• Databricks

Databases

• PostgreSQL
• MySQL
• MongoDB
• Snowflake