Role & responsibilities

• Design, develop, and deploy ML pipelines using contemporary MLOps frameworks (MLflow, Kubeflow, Vertex AI, SageMaker, Azure ML).
Implement and fine-tune LLMs, Generative AI models, and Transformer architectures for diverse business use cases.
Optimize model training, inference, and deployment workflows across cloud settings (AWS, Azure, GCP).
Collaborate with data scientists and engineers to ensure seamless integration of AI models into production systems.
Monitor, troubleshoot, and improve model performance, scalability, and reliability.
Stay updated with the latest advancements in Generative AI, LLMs, and MLOps practices.

Preferred candidate profile

• Solid proficiency in Python (TensorFlow, PyTorch,



Hugging Face Transformers).

• Hands-on experience with MLOps tools (MLflow, Kubeflow, Airflow, CI/CD pipelines).

• Deep understanding of LLMs, Generative AI, and Transformer models.

• Cloud expertise in AWS (SageMaker, EC2, S3, Lambda), Azure (Azure ML, Databricks), or GCP (Vertex AI, BigQuery).

• Experience with containerization and orchestration (Docker, Kubernetes).

• Solid grasp of data engineering workflows and scalable ML infrastructure.

• Strong problem-solving and communication skills.

📌 Core Ai Engineer Gurugram (India)
🏢 StatusNeo Technology Consulting
📍 India