AI Engineer

We are seeking a highly skilled AI Engineer with a robust background in full stack development to lead the design, implementation, and maintenance of an MLOps platform supporting real-time AI and advanced analytics applications. This role is ideal for someone passionate about building scalable AI systems, with a preference for on-premises deployments, and exposure to SCADA systems and Battery Energy Storage Systems (BESS).

Key Responsibilities:

• MLOps Platform Development: Design and implement a scalable and secure MLOps platform for real-time AI applications.
• Cross-Team Collaboration: Work closely with data scientists, developers, and IT teams to streamline model deployment and monitoring.
• Pipeline Automation: Develop automated pipelines for ML model training, validation, deployment, and monitoring.
• System Integration: Integrate the platform with data lakes, warehouses, and on-premise infrastructure.
• Performance Tuning: Monitor AI model and platform performance, identify bottlenecks, and implement optimizations.
• Support Environments: Provide ongoing support for both development and production systems.
• Documentation: Maintain comprehensive documentation of architecture, workflows, and standard operating procedures.
• Continuous Learning: Stay up to date with emerging trends in AI, MLOps, DevOps, and relevant tools/technologies.

Required Qualifications:

• Experience: 5+ years in AI engineering or full stack development roles; strong hands-on experience in building MLOps platforms.
• Programming Languages: Proficient in Python and JavaScript/TypeScript.
• AI/ML Frameworks: Hands-on with TensorFlow, PyTorch, scikit-learn.
• Containerization & Orchestration: Strong experience with Docker and Kubernetes.
• CI/CD Tools: Familiarity with Jenkins, GitLab CI, or CircleCI.
• Data Engineering: Knowledge of ETL processes, data pipeline development, and large-scale data integration.
• Databases: Experience with both SQL and NoSQL (e.g., MongoDB, Cassandra).
• Deployment: Experience in on-premises infrastructure deployment.
• Real-Time Processing: Exposure to Apache Kafka and Apache Flink.
• DevOps: Solid grasp of DevOps practices and infrastructure automation.

Preferred Qualifications:

• MLOps Tools: Experience with MLflow, Kubeflow, or TensorFlow Extended (TFX).
• Monitoring & Logging: Familiarity with Prometheus, Grafana, or the ELK stack.
• Web Development: Experience with front-end frameworks such as React, Angular, or Vue.js.
• Data Visualization: Proficiency in Power BI, Tableau, or Alteryx.
• SCADA Systems: Prior experience with Supervisory Control and Data Acquisition (SCADA).
• BESS: Exposure to Battery Energy Storage Systems in AI/ML implementations.
• Education: Bachelor's or Master’s in Computer Science, Engineering, or a related field.

Certifications (if any):

[Optional, e.g., AWS Certified Machine Learning – Specialty, Microsoft Certified: Azure AI Engineer Associate, etc.]

Tech Stack Overview:

Programming         Python, JavaScript/TypeScript

ML Frameworks TensorFlow, PyTorch, scikit-learn

CI/CD & DevOps Jenkins, GitLab CI, CircleCI

Containerization Docker, Kubernetes

Real-Time Data Apache Kafka, Apache Flink

Databases         SQL, MongoDB, Cassandra

Cloud Platforms AWS, Azure, GCP

MLOps Tools         MLflow, Kubeflow, TFX

Monitoring         Prometheus, Grafana, ELK Stack

Web Frameworks React, Angular, Vue.js (Preferred)

Visualization Tools Power BI, Tableau, Alteryx

Specialized Systems SCADA Systems, BESS