Senior MLOps Engineer - Python Programming

Role : Senior MLOps Engineer

We are looking for a highly experienced and self-driven Senior MLOps Engineer to join our AI/ML engineering team.

This role involves the full lifecycle of machine learningfrom data ingestion and model development to deployment and maintenancefocusing on solving real-world business problems with AI.

You will play a crucial role in building scalable ML/LLM pipelines and deploying cutting-edge AI solutions including generative AI, NLP, and graph-based learning.

Key Responsibilities :

- Design and develop ML pipelines for ingestion, processing, modeling, deployment, and retraining of models for structured and unstructured data.

- Implement and manage MLOps practices using tools such as MLflow, Kubeflow, and TensorFlow Serving.

- Build and maintain CI/CD/CT/CM pipelines for model deployment and monitoring using tools like GitHub Actions, Docker, and Kubernetes.

- Develop applications leveraging deep learning, NLP, LLMs, and other AI techniques to solve business challenges.

- Collaborate with data scientists and engineers to convert business problems into data-driven ML solutions.

- Manage model lifecycle (PLM) including versioning, monitoring, and retraining for optimal performance in production.

- Build and deploy models as RESTful APIs using frameworks like Flask or Django.

- Ensure robustness and scalability in deployment of generative AI models, especially GPT, Transformers, and GNNs.

- Identify and resolve anomalies in data distribution and model effectiveness through exploration and visualization.

- Stay current with advancements in ML/AI and contribute to team knowledge sharing and upskilling.

Required Qualifications & Skills :

- 5+ years of hands-on experience in Machine Learning, Deep Learning, NLP, and MLOps.

- Strong understanding of MLOps tools such as MLflow, Kubeflow, TensorFlow Serving, Airflow, etc.

- Experience in prompt engineering and fine-tuning of LLMs (e.g., GPT models).

- Proficient in Python and data manipulation using SQL or similar.

- Hands-on experience with containerization (Docker) and orchestration (Kubernetes).

- Experience working with cloud platforms like AWS, Azure, or GCP.

- Proficiency in developing and maintaining machine learning APIs using Flask/Django.

- Familiarity with graph neural networks (GNN) and transformer-based architectures.

- Excellent skills in data visualization, debugging, and anomaly detection in ML pipelines.

Education :

- Masters degree in Computer Science, Mathematics, Statistics, or a related field