- Company Name
- Provation
- Job Title
- AI/ML Engineer
- Job Description
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**Job Title:** AI/ML Engineer
**Role Summary:**
Design, develop, and deploy scalable machine‑learning models and AI solutions that integrate with product platforms. Lead MLOps workflows, ensure data pipeline efficiency, and uphold security, privacy, and ethical standards across cloud and containerized environments. Collaborate with product, data science, DevOps, and engineering teams in an Agile setting.
**Expectations:**
- Deliver production‑grade ML models meeting performance, compliance, and reliability goals.
- Maintain end‑to‑end AI/ML pipelines from data ingestion to inference.
- Stay current with state‑of‑the‑art techniques (deep learning, NLP, computer vision, generative AI).
- Contribute to code reviews, sprint planning, and continuous improvement initiatives.
**Key Responsibilities:**
1. Design, train, optimize, and launch ML models for diverse domains (NLP, CV, time‑series).
2. Implement data preprocessing, feature engineering, and model evaluation frameworks.
3. Build and manage scalable data pipelines supporting training and real‑time inference.
4. Develop MLOps processes using tools such as MLflow, Kubeflow, or Airflow for model tracking, monitoring, and automated retraining.
5. Deploy models in cloud (Azure ML/AI) and containerized platforms (Docker, Kubernetes) and integrate with .NET microservices.
6. Ensure compliance with HIPAA, GDPR, and AI ethics guidelines.
7. Collaborate cross‑functionally with product managers, data scientists, and software engineers.
8. Participate in Agile ceremonies and maintain documentation of AI solutions.
**Required Skills:**
- Python proficiency; experience with TensorFlow, PyTorch, or Scikit‑learn.
- Hands‑on development of generative AI/LLMs and deep learning models.
- Cloud AI/ML platforms (Azure ML, Azure AI Foundry).
- MLOps tools: MLflow, Kubeflow, Airflow (or equivalent).
- Containerization and orchestration: Docker, Kubernetes.
- C#/.NET integration experience.
- Strong foundation in data structures, algorithms, and distributed computing.
- Knowledge of AI ethics, data privacy, and security best practices.
**Required Education & Certifications:**
- Bachelor’s or Master’s degree in Computer Science, Artificial Intelligence, Machine Learning, or a related discipline, or equivalent professional experience.
- No specific certifications mandated, but certifications in Azure AI, TensorFlow, or related MLOps tools are advantageous.