AI Engineer - Model Building | GenAI | MLOps

• Design, build, and optimize   machine learning and deep learning models   using structured and unstructured data.
• Develop and fine-tune   LLMs and Generative AI models   using   PyTorch   and relevant NLP frameworks.
• Implement scalable   MLOps pipelines   including data ingestion, training workflows, validation, and deployment.
• Deploy ML models to production on   cloud platforms   like   AWS, Azure, or GCP   using containerization and CI/CD pipelines.
• Automate and monitor model lifecycle using tools such as   MLflow, DVC, Kubeflow, Airflow , or equivalents.
• Perform advanced   data wrangling and preprocessing   using   SQL and Python .
• Optimize models for performance, latency, and cost-efficiency in production environments.
• Work with   large datasets , distributed systems, and high-throughput data pipelines using Spark.

Required Skills:

• Strong hands-on skills in   Python   (NumPy, Pandas, Scikit-learn, PyTorch).
• Advanced   SQL   knowledge for data manipulation and analytics.
• Experience with   model training, hyperparameter tuning , and validation at scale.
• Practical exposure to   LLMs , RAG, prompt engineering, embeddings, and vector stores.
• Proficiency in deploying models using   Docker, Kubernetes , and   CI/CD pipelines .
• Familiarity with   cloud services   (SageMaker, Vertex AI, Azure ML, etc.) for training and deployment.
• Experience with   monitoring, logging, and managing ML workflows   in production.

Preferred Qualifications:

• Degree in   Data Science, Statistics, Mathematics, or Computer Science .
• Experience with   Hugging Face, LangChain, OpenAI APIs , or similar LLM tools.
• Exposure to   experiment tracking, reproducibility tools , and   Git-based workflows .
• Prior experience working on   large-scale data or enterprise AI systems .