AI / Senior Machine Learning Engineer

The AI / Senior Machine Learning Engineer acts as the technical architect responsible for the design, training, optimization, and deployment of machine learning algorithms. This individual translates theoretical data models into robust, low-latency enterprise software infrastructure capable of powering 24/7 automated business tools across various communication streams

Detailed Duties & Responsibilities

• ML Model Architecture & Training: Build and scale custom Machine Learning algorithms and natural language pipelines .Focus on predictive analytics, text processing, intent interpretation, and omnichannel workflows
• Production MLOps Infrastructure: Own complete production deployment cycles, utilizing containerization mechanisms and robust Continuous Integration / Continuous Deployment (CI/CD) practices
• Telemetry & System Observability: Construct and scale live engineering dashboards to observe system latency, query throughput, model accuracy degradation, and data drift over time
• Operationalizing Data Frameworks: Collaborate closely with investigative Data Scientists to transform raw prototypes into enterprise-grade features integrated with Customer Data Platforms (CDP)
• Data Manipulation & Pipeline Quality: Oversee vast structured and unstructured communications data sets. Conduct feature engineering, data transformations, and comprehensive technical QA
• System Compliance & Governance: Generate exhaustive code documentation and architectural blueprints to maintain regulatory compliance for operations within highly audited environments, such as financial and insurance sectors

• Minimum Education: Bachelor's or Master's Degree in Computer Science, Machine Learning, Data Analytics, or a highly related quantitative engineering field

Mandatory Experience & Skills Level

• Experience Required: Minimum of 5 years of proven experience building, testing, and deploying machine learning models directly into production environments).
• Tooling Proficiency: Advanced operational mastery of MLOps tools (such as MLflow) and observability systems (such as Prometheus, Grafana, ELK, or Datadog) .
• Languages & Libraries: Absolute proficiency in Python development alongside core data frameworks (scikit-learn, XGBoost, TensorFlow, PyTorch, Pandas, NumPy, and advanced SQL querying).