Job Description
We are seeking a talented and motivated
Machine Learning Engineer with a focus on
Generative AI and
MLOps to join our dynamic team. The ideal candidate will design, develop, and deploy machine learning models, leveraging generative AI techniques and operational best practices. You will collaborate closely with data scientists, software engineers, and product teams to deliver scalable AI-driven solutions that enhance business outcomes and customer experiences.
Key Responsibilities
- Design, build, and optimize machine learning and generative AI models, including model fine-tuning, embedding generation, and evaluation.
- Develop and maintain MLOps pipelines for model deployment, monitoring, retraining, and scaling across production environments.
- Implement data preprocessing, feature extraction, labeling, and augmentation workflows for robust model performance.
- Deploy and manage models in containerized environments using Docker and Kubernetes, integrating with cloud AI services (AWS, Azure, or GCP).
- Collaborate with cross-functional teams to identify, prototype, and productionize AI-driven solutions aligned with business objectives.
- Conduct A/B testing, monitor model drift and performance, and iterate model improvements.
- Implement data versioning, feature store management, and experiment tracking to ensure reproducibility.
- Maintain comprehensive documentation for algorithms, data pipelines, and model lifecycle management.
- Stay current with advancements in machine learning, generative AI, and MLOps practices.
Skills And Tools Required
- Solid foundation in Python, NumPy, Pandas, Scikit-learn, and Matplotlib for data analysis and model development.
- Strong hands-on experience with TensorFlow or PyTorch for ML/DL model development.
- Understanding of AI model serving, API exposure, and inference optimization for real-time predictions.
- Familiarity with MLOps principles and tools such as MLflow, Kubeflow, and DVC for model orchestration and version control.
- Exposure to Generative AI techniques, including transformer architectures, GANs, VAEs, and fine-tuning for domain-specific tasks.
- Basic understanding of RAG (Retrieval-Augmented Generation) pipelines, vector databases, and similarity search mechanisms.
- Experience in A/B testing, model monitoring, and cloud-based ML operations.
- Knowledge of data versioning and feature store tools such as Feast or Tecton.
- Familiarity with version control systems like Git and CI/CD practices for ML workflows.
Tools & Technologies
- Machine Learning Frameworks: TensorFlow, PyTorch, Keras
- Data Processing Libraries: Pandas, NumPy, Scikit-learn
- Visualization Tools: Matplotlib, Seaborn
- MLOps Tools: MLflow, Kubeflow, DVC, Airflow
- Feature Stores & Data Versioning: Feast, Tecton
- Containerization & Orchestration: Docker, Kubernetes
- CI/CD & Automation: Jenkins, GitHub Actions
- Cloud Platforms: AWS, Azure, or GCP (AI/ML Services)
- Vector Databases: FAISS, Pinecone, Chroma (for embedding-based search)
Preferred Qualifications
- Experience working on projects involving Generative AI applications.
- Familiarity with containerization technologies like Docker and orchestration tools such as Kubernetes.
- Understanding of data privacy and ethical considerations in AI and machine learning.
If you are passionate about leveraging machine learning to create innovative solutions and have a keen interest in Generative AI and MLOps, we encourage you to apply and join our forward-thinking team.
