Machine Learning Engineer

Job Description:

Role:  ML Engineer

Experience: 4+ Years

Location: Bagmane World Tech Center [Hybrid] or Remote [for exceptionally good candidate]
Mandatory Skills: Azure Cloud Platform, Python with OOPs concept, ML Algorithms
(Supervised/Unsupervised, Forecasting, etc.), Databricks including Unity Catalogue, Airflow,
MLOPs, ML Lifecycle, CI/CD Pipeline, Flask/FastAPI

We are seeking an experienced ML Engineer to join our team and contribute to the Logistics
Analytics domain. The ideal candidate will have a strong background in Traditional ML (e.g.,
Forecasting), end-to-end ML/DL project execution, and real-world deployment in industries such
as finance, healthcare, retail, or technology. You will play a key role in designing, developing,
and deploying scalable ML models on Azure, while ensuring best practices in MLOps, CI/CD,
and the full ML lifecycle.

Responsibilities

End-to-End ML Projects: Lead the development and deployment of ML/DL models from

1. data collection to production, ensuring scalability and reliability.

2. Model Deployment: Deploy ML models using Flask, FastAPI, or cloud-based solutions
(AWS SageMaker, Google AI Platform, Azure ML).

3. Apply ML/DL techniques to solve business problems in logistics and other domains.
4. Design, develop, and deploy ML models on Azure, leveraging services like Azure ML,
Databricks, and Airflow.
5. Write modular, reusable Python code following Object-Oriented Programming (OOP)
principles.
6. Implement MLOps best practices, including model versioning, monitoring, and CI/CD
pipelines for automated deployments.
7. Build and optimize data pipelines using Databricks and orchestrate workflows with
Airflow.
8. Oversee the entire ML lifecycle—data preprocessing, feature engineering, model
training, validation, deployment, and monitoring.
9. Manage data governance, access control, and data quality using Databricks UC.
10. Work with data scientists, engineers, and business teams to deliver ML solutions aligned
with business needs.

Requirements

1. Bachelor’s/Master’s in Computer Science, Data Science, or related fields.
2. 4+ years in ML Engineering, with hands-on experience in model deployment, MLOps,
and cloud platforms (Azure preferred).
3. Experience in logistics, supply chain, finance, healthcare, or retail is a plus.
4. Strong problem-solving, collaboration, and communication skills.
5. Experience with Deep Learning (TensorFlow/PyTorch).
6. Knowledge of big data technologies (Spark, Hadoop).

7. Familiarity with Kubernetes, Docker for containerized deployments.