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 environments (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

• Strong 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)
🏢 StatusNeo Technology Consulting
📍 Gurugram