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We are seeking an experienced AI / Machine Learning Engineer to design, build, and operate intelligent systems that drive automation, insight, and real-time decision-making across the business. This role focuses on practical, production-grade AI — not research prototypes — with an emphasis on reliability, governance, and measurable impact. You will collaborate across engineering, product, and operations teams to create ML-powered workflows, conversational experiences, predictive models, and automation frameworks that improve efficiency and user outcomes. This is an applied AI role embedded deeply in real systems and real workflows.
Job Responsibility:
Build AI-driven conversational experiences and automated decision logic that operate in real time
Design secure data integrations that enable intelligent responses across distributed systems
Optimize large language model (LLM) workflows to balance accuracy, latency, cost, and usability
Apply ML techniques to both structured and unstructured datasets
Develop models for trend detection, anomaly identification, and proactive operational signaling
Implement monitoring and alerting to identify model or system issues early
Embed AI-powered insights directly into internal tools used by operations and support teams
Deliver contextual recommendations, next-best actions, and dynamic guidance based on live system data
Increase team productivity through intelligent assistance and knowledge retrieval
Build automated diagnostics and remediation workflows with strong safety and audit controls
Ensure all AI-driven actions respect authorization policies, security standards, and compliance expectations
Create dashboards and reporting for system health, workflow execution, and model performance
Requirements:
3+ years of professional experience in AI/ML engineering, data science, or intelligent automation
Strong proficiency in Python, with experience using ML frameworks such as TensorFlow, PyTorch, or scikit-learn
Hands-on experience with modern cloud platforms and managed compute, data, and AI services
Experience building real-time, streaming, or event-driven data pipelines
Track record of deploying and operating production-grade ML systems
Solid understanding of APIs, microservices, and secure integration patterns
Nice to have:
Background working with IoT data, telemetry, or distributed sensor systems
Experience with LLM tuning, retrieval-augmented generation (RAG), or conversational AI workflows
Familiarity with enterprise workflow automation or customer support platforms
Understanding of authentication, authorization, and access-control frameworks
Experience balancing AI innovation with operational reliability and governance