AI/ML Engineer

Al/ML (4-6 Exp) : CV send to : [Confidential Information] / 8897851850

We are seeking an AI/ML Engineerto design, build, deploy, and scale machine learning solutionsthat solve real-world business problems. The ideal candidate will work across the full ML lifecyclefrom data preparation and model development to production deployment and monitoring.

Key Responsibilities

Design, develop, and deploy machine learning modelsfor structured and unstructured data.

Build and maintain end-to-end ML pipelinesincluding data ingestion, training, evaluation, and deployment.

Work with large datasets and perform feature engineering and data preprocessing.

Deploy ML models into production using REST APIs or microservices.

Monitor model performance, drift, and retraining strategies.

Collaborate with product, data, and engineering teams to define AI-driven solutions.

Optimize models for performance, scalability, and reliability.

Ensure responsible AI practices, data privacy, and security compliance.

Required Skills & Qualifications

Strong programming skills in Python.

Solid understanding of machine learning algorithms(supervised, unsupervised, ensemble methods).

Hands-on experience with ML frameworks(TensorFlow, PyTorch, Scikit-learn).

Experience in data manipulation using Pandas, NumPy, and SQL.

Knowledge of statistics, probability, and model evaluation techniques.

Experience deploying ML models in production environments.

Familiarity with Git, CI/CD pipelines, and Docker.

Cloud & Platform Experience (At Least One Required)

GCP:Vertex AI, BigQuery, Cloud Storage

AWS:SageMaker, S3, EC2

Azure:Azure ML, Azure Data Services

Good to Have

Experience with MLOps tools(MLflow, Kubeflow, Airflow).

Exposure to Deep Learning, NLP, or Computer Vision.

Experience with streaming data(Kafka, Pub/Sub).

Knowledge of GenAI / LLMsis a plus.

Cloud certifications (GCP / AWS / Azure).

Nice to Have (Bonus)

Experience in recommendation systems or forecasting models.

Understanding of data engineering concepts.

• Exposure to business KPIs and analytics-driven decision making.