Platform Engineer III

Job Summary

We are looking for a highly skilled Machine Learning Engineer to design, develop, deploy, and optimize AI/ML solutions that solve real-world business challenges. The ideal candidate will have strong expertise in machine learning algorithms, data engineering, MLOps, model deployment, and cloud-based AI platforms. The role involves working closely with data scientists, software engineers, and business stakeholders to build scalable AI products and intelligent automation solutions.

Key Responsibilities

Design, develop, train, and deploy machine learning models for business and industrial use cases.

Build end-to-end ML pipelines covering data ingestion, feature engineering, model training, validation, deployment, and monitoring.

Develop predictive analytics, recommendation systems, anomaly detection, forecasting, and classification solutions.

Work with structured, semi-structured, and unstructured datasets.

Optimize model performance, accuracy, scalability, and inference latency.

Implement MLOps practices for continuous training, deployment, and monitoring.

Collaborate with software engineering teams to integrate ML models into enterprise applications.

Evaluate and benchmark machine learning and deep learning models.

Develop proof-of-concepts and production-ready AI solutions.

Stay updated with the latest advancements in AI, Machine Learning, Deep Learning, and Generative AI.

Skill Requirements

Machine Learning

Supervised & Unsupervised Learning

Classification & Regression

Clustering Techniques

Ensemble Methods

Recommendation Systems

Time Series Forecasting

Statistical Modeling

Feature Engineering

Deep Learning

Neural Networks

CNN, RNN, LSTM

Transformers

Computer Vision Models

NLP Models

Transfer Learning

Python (Mandatory)

R (Good to Have)

ML Frameworks

Scikit-learn

TensorFlow

PyTorch

XGBoost

LightGBM

Data Engineering

Pandas

NumPy

Data Processing Pipelines

MLOps & Deployment

MLflow

Model Monitoring

Model Versioning

Other Requirements

Kubeflow

Docker

Kubernetes

CI/CD Pipelines

Cloud Platforms

Microsoft Azure Machine Learning

Azure AI Services

AWS SageMaker

Google Vertex AI

Databases

SQL Server

PostgreSQL

MongoDB