Artificial Intelligence/Machine Learning Engineer - PyTorch/Data Ingestion

We are seeking a highly skilled AI/ML Engineer with strong expertise in Python programming, API development, and real-time deployment of ML models. The ideal candidate should have experience in designing, building, and optimizing machine learning pipelines and integrating models into production environments.

Key Responsibilities :

- Design, develop, and optimize machine learning models using Python and popular ML frameworks (TensorFlow, PyTorch, Scikit-learn, etc.).

- Implement end-to-end ML pipelines including data preprocessing, feature engineering, training, evaluation, and deployment.

- Build and manage RESTful APIs / gRPC services to expose ML models for real-time inference.

- Deploy and scale models in production environments (Docker, Kubernetes, cloud platforms such as AWS, GCP, or Azure).

- Ensure high availability, low-latency, and fault-tolerant real-time ML systems.

- Collaborate with data engineers and software teams to integrate ML solutions into existing applications.

- Conduct performance monitoring, optimization, and retraining of models as needed.

- Apply MLOps best practices for CI/CD pipelines, model versioning, and automated deployment workflows.

- Write clean, efficient, and production-grade Python code following software engineering best practices.

Required Skills & Experience :

- 3-8 years of hands-on experience in Python programming (advanced knowledge of data structures, OOP, multiprocessing, async programming).

- Strong expertise in machine learning algorithms, model training, and evaluation techniques.

- Experience with API development (FastAPI, Flask, Django, or similar).

- Proven experience in real-time model deployment and serving (TensorFlow Serving, TorchServe, MLflow, or custom solutions).

- Solid understanding of cloud-native deployments (AWS Sagemaker, GCP Vertex AI, Azure ML) and containerization (Docker, Kubernetes).

- Knowledge of streaming data frameworks (Kafka, Spark Streaming, Flink) is a plus.

- Familiarity with CI/CD pipelines for ML (GitHub Actions, Jenkins, or similar).

- Strong grasp of data engineering concepts (data ingestion, transformation, and storage).

- Experience in monitoring & logging (Prometheus, Grafana, ELK, or equivalent).

Nice to Have :

- Experience with Generative AI (LLMs, diffusion models, transformers).

- Exposure to GPU optimization and distributed training.

- Familiarity with feature stores and advanced MLOps frameworks (Kubeflow, TFX, MLflow).