AI Engineer – Model Building | GenAI | MLOps

AI Engineer – Model Building | GenAI | MLOps

Experience:

4 to 8 years

Location:

Job Type:

Technical Responsibilities:

• 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.