Bell Techlogix

The AI Data Engineer will be responsible for designing, building, and operating scalable data pipelines and curated data assets that power machine learning, generative AI, and intelligent automation solutions in an SLA-driven managed services environment. This role focuses on data ingestion, transformation, governance, and operational reliability across cloud and hybrid environments enabling use cases such as knowledge retrieval (RAG), conversational AI, predictive analytics, and AI-assisted service management. The ideal candidate combines strong data engineering fundamentals with an understanding of AI workload requirements, including quality, lineage, privacy, and performance. 

Key Responsibilities 

•Design, build, and operate production-grade data pipelines that support AI/ML and generative AI workloads in managed services environments 

•Develop curated, analytics-ready datasets and data products to enable model training, grounding, feature generation, and AI search/retrieval 

•Implement data ingestion patterns for structured and unstructured sources (APIs, databases, files, event streams, documents) 

•Build and maintain transformation workflows with strong testing and validation 

•Enable Retrieval-Augmented Generation (RAG) by preparing document corpora, chunking strategies, metadata enrichment, and vector indexing patterns 

•Integrate data pipelines with application services 

•Support ITSM and enterprise workflow data needs, including ServiceNow data integration, CMDB/incident data quality improvements, and automation enablement 

•Implement observability for data pipelines (monitoring, alerting, SLAs/SLOs) and perform root cause analysis for pipeline failures or data quality incidents 

•Apply data governance and security best practices 

•Collaborate with ML Engineers, DevOps/SRE, and solution architects to operationalize end-to-end AI solutions 

•Contribute to reusable patterns, templates, and standards within the Bell Techlogix AI Center of Excellence 

Required Qualifications 

•Bachelor’s degree in Computer Science, Engineering, Information Systems, or equivalent practical experience 

•5+ years of experience in data engineering, analytics engineering, or platform data operations 

•Strong proficiency in SQL and Python; experience with data modeling and dimensional concepts 

•Hands-on experience with Azure data services (e.g., Data Factory, Synapse, Databricks, Storage, Key Vault) or equivalent cloud tooling 

•Experience building reliable pipelines with scheduling, dependency management, and automated testing/validation 

•Experience supporting production data platforms with incident management, troubleshooting, and root cause analysis 

•Understanding of data security, privacy, and governance principles in enterprise environments 

Preferred Qualifications 

•Experience enabling AI/ML workloads: feature engineering, training data preparation, and integration with Azure Machine Learning 

•Experience with unstructured data processing for generative AI 

•Familiarity with vector databases or vector search and RAG patterns 

•Experience with event streaming and messaging 

•Familiarity with ServiceNow data model and integration patterns (Table API, export, CMDB/ITSM reporting) 

•Relevant certifications (Microsoft Azure Data Engineer, Azure AI Engineer, Databricks) 

The DevOps Engineer will play a critical role in operationalizing artificial intelligence across Bell Techlogix client environments. This role focuses on building and supporting cloud infrastructure, CI/CD pipelines, and automation frameworks that power AI and machine learning workloads. The ideal candidate has experience supporting AI platforms such as Azure AI, Azure Machine Learning, Azure OpenAI, and ServiceNow or conversational AI platforms, and understands the operational requirements of production AI systems, including reliability, scalability, and security. 

Key Responsibilities 

•Design, build, and operate cloud infrastructure and platform services that support AI and machine learning workloads in production, SLA-driven managed services environments 

•Implement CI/CD and MLOps pipelines to enable automated training, testing, deployment, and rollback of AI and ML models 

•Develop and maintain Infrastructure as Code to provision AI-ready environments consistently across dev/test/prod 

•Support AI platform operations including monitoring model health, pipeline execution, compute utilization, and data dependencies 

•Partner with Machine Learning Engineers and Data Engineers to standardize deployment patterns for AI services and LLM-based solutions 

•Enable secure and scalable AI integrations using APIs, messaging, and event-driven architectures 

•Implement observability solutions for AI platforms, including logging, metrics, alerting, and drift detection integrations 

•Troubleshoot AI platform incidents, perform root cause analysis, and implement remediation to improve reliability and automation coverage 

•Apply security best practices for AI environments including secrets management, identity and access controls, network isolation, and policy enforcement 

•Support AI-driven automation use cases across platforms such as Microsoft Copilot, ServiceNow, and conversational AI tools 

•Collaborate with service desk, security, and architecture teams to continuously improve AI service delivery and operational maturity 

Required Qualifications 

•Bachelor’s degree in Computer Science, Engineering, or equivalent practical experience 

•5+ years of experience in DevOps, cloud engineering, or platform operations, with exposure to AI or data workloads 

•Hands-on experience with Microsoft Azure, including compute, networking, storage, and monitoring services 

•Experience building CI/CD pipelines using Azure DevOps, GitHub Actions, or similar tools 

•Working knowledge of Infrastructure as Code (Terraform and/or Bicep/ARM) 

•Scripting experience using PowerShell and/or Python 

•Experience supporting production platforms with incident management, change control, and root cause analysis 

•Understanding of cloud security fundamentals and enterprise governance requirements 

Preferred Qualifications 

•Experience with Azure Machine Learning, Azure AI Services, Azure OpenAI, or MLOps frameworks 

•Exposure to containerization and orchestration technologies (Docker, Kubernetes, AKS) 

•Experience supporting data pipelines or feature stores used by machine learning systems 

•Familiarity with ServiceNow, AI-driven ITSM workflows, or automation platforms 

•Experience with observability tools 

•Knowledge of Responsible AI, data governance, and compliance considerations for AI systems 

•Relevant certifications (Microsoft Azure Administrator, Azure DevOps Engineer, Azure AI Engineer)

The Machine Learning Engineer will play a critical role in supporting Bell Techlogix clients by building, operating, and optimizing AI solutions in a managed services environment. This role focuses on delivering reliable, secure, and scalable AI capabilities across Microsoft AI platforms, Kore.ai conversational AI, and ServiceNow, while also supporting broader AI initiatives and the AI Center of Excellence. 

Key Responsibilities 

•Design, deploy, and support machine learning and AI solutions in production, SLA-driven managed services environments 

•Provide operational support for AI platforms including incident response, troubleshooting, and root cause analysis 

•Monitor AI and ML model performance, data quality, and drift; implement retraining and optimization strategies 

•Build and maintain MLOps pipelines supporting model training, validation, deployment, and rollback 

•Develop and support AI workloads using Microsoft Azure AI, Azure Machine Learning, Azure OpenAI, and Copilot extensibility 

•Design, train, and optimize virtual assistants enterprise workflows 

•Implement and support AI capabilities including Predictive Intelligence, Virtual Agent, and AI Search 

•Collaborate with service desk, engineering, security, and platform teams to drive automation and continuous service improvement 

•Act as a technical escalation point for AI-related client issues and enhancement requests 

•Contribute to AI innovation initiatives, proofs of concept, and reusable solution patterns within Bell Techlogix 

Required Qualifications 

•Bachelor’s degree in Computer Science, Data Science, Machine Learning, or equivalent practical experience 

•5+ years of experience in machine learning engineering, AI development, or applied data science 

•Strong proficiency in Python, SQL, and API-based integrations 

•Hands-on experience supporting machine learning models in production environments 

•Experience working in managed services, consulting, or enterprise IT environments 

•Strong understanding of cloud platforms (Microsoft Azure preferred) 

Preferred Qualifications 

•Experience with Azure Machine Learning, Azure AI Services, or Azure OpenAI 

•Hands-on experience with Kore.ai XO Platform or enterprise conversational AI 

•Experience implementing or supporting ServiceNow AI/ML, Predictive Intelligence, or Virtual Agent 

•Familiarity with MLOps, CI/CD pipelines, Infrastructure as Code (Terraform, Bicep, ARM) 

•Knowledge of Responsible AI, data governance, and enterprise security practices 

•Relevant certifications (Microsoft, ServiceNow, Kore.ai)