Machine Learning Engineer

Key Responsibilities :

- Design, build, and deploy end-to-end ML models into production.

- Collaborate with data scientists to optimize algorithms and feature engineering.

- Develop data pipelines and ETL workflows for training and inference.

- Implement scalable ML systems using cloud services (AWS, GCP, Azure).

- Optimize models for performance, scalability, and latency.

- Monitor, retrain, and maintain ML models in production.

- Work on projects involving NLP, Computer Vision, and Deep Learning.

- Ensure ML solutions follow best practices for security, compliance, and reliability.

- Collaborate with cross-functional teams including product managers, analysts, and engineers.

- Stay updated with the latest ML frameworks, libraries, and research advancements.

Qualifications & Skills :

- Bachelors/Masters degree in Computer Science, Data Science, AI/ML, or related field.

- 5+ years of experience in ML engineering or applied ML roles.

- Strong proficiency in Python and ML libraries (scikit-learn, TensorFlow, PyTorch, Keras).

- Experience with data manipulation & querying (Pandas, SQL, Spark).

- Strong knowledge of NLP, Computer Vision, or Deep Learning architectures.

- Hands-on with cloud ML services (AWS SageMaker, GCP Vertex AI, Azure ML).

- Familiarity with Docker, Kubernetes, and CI/CD pipelines.

- Strong understanding of MLOps practices (model versioning, monitoring, retraining).

- Experience with API development and integration (REST/GraphQL).

- Excellent problem-solving and analytical skills with a growth mindset.

Nice to Have :

- Knowledge of big data frameworks (Hadoop, Spark, Databricks).

- Exposure to streaming data platforms (Kafka, Flink).

- Experience in recommendation systems, reinforcement learning, or generative AI.