Job Summary :

The Machine Learning Engineer is responsible for designing, developing, deploying, and maintaining machine learning and Al solutions that drive business value through predictive analytics, intelligent automation, anomaly detection, and advanced data-driven insights. This role requires strong expertise in data science, machine learning algorithms, MLOps, cloud technologies, and software engineering practices.

Key Responsibilities :

1. Machine Learning Solution Development :

- Design, develop, and implement Machine Learning and Artificial Intelligence solutions for business and security use cases.

- Build predictive, classification, clustering, anomaly detection, recommendation, and forecasting models.

- Develop and optimize machine learning pipelines for large-scale datasets.

- Evaluate and compare different algorithms to select the most effective solution.

- Perform feature engineering, model training, validation, and performance tuning.

2. Data Engineering & Analytics :

- Collect, process, clean, and transform structured and unstructured data from multiple sources.

- Perform exploratory data analysis (EDA) to identify trends, correlations, and insights.

- Design scalable data pipelines to support model training and inference.

- Collaborate with data engineers and business stakeholders to ensure data quality and availability.

3. Model Deployment & MLOps :

- Deploy machine learning models into production environments.

- Build and maintain automated ML pipelines for continuous integration, testing, monitoring, and retraining.

- Monitor model performance, drift, and accuracy over time.

- Implement MLOps best practices for version control, experimentation tracking, model governance, and lifecycle management.

- Develop APIs and services for model consumption by business applications.

4. AI & Advanced Analytics :

- Leverage Generative AI, Large Language Models (LLMs), and NLP technologies where applicable.

- Build intelligent automation solutions by integrating AI models with business workflows.

- Research and evaluate emerging AI technologies and frameworks.

- Develop proof-of-concepts (POCs) and pilots to demonstrate business value.

5. Collaboration & Stakeholder Management :

- Work closely with business teams to understand requirements and translate them into AI solutions.

- Present technical findings and model outcomes to both technical and non-technical audiences.

- Collaborate with cross-functional teams including automation, cloud, security, application development, and analytics teams.

- Support AI solution adoption through documentation, training, and knowledge sharing.

Qualifications :

- Bachelor's degree in Computer Science, Data Science, Artificial Intelligence, Information Technology, Mathematics, Statistics, or a related field. Master's degree is preferred.

- Strong experience in Machine Learning model development and deployment.

- Hands-on experience with Python and ML frameworks such as Scikit-learn, TensorFlow, or PyTorch.

- Experience with the Azure AI ecosystem, Azure ML, Azure OpenAI, and cloud-native AI solutions.

- Understanding of MLOps practices, model lifecycle management, and production deployment.

- Strong problem-solving, analytical, and troubleshooting skills.

- Excellent communication and stakeholder management abilities.

- Ability to work independently and collaboratively in a fast-paced environment.