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Artificial Intelligence / Machine Learning Engineer - PyTorch / Data Ingestion

Artificial Intelligence / Machine Learning Engineer - PyTorch / Data Ingestion

MindSource TechnologiesGurgaon
19 hours ago
Job description

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).
  • (ref : hirist.tech)

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