Role overview
The AI Engineer plays a central role in designing, developing, and integrating AI-driven capabilities across Lightedge’s core data and operational systems. This position bridges strategy and execution—translating business problems into scalable AI solutions that enhance automation, analytics, and decision-making.
The ideal candidate combines technical expertise in AI/ML, systems integration, and data architecture with strong business acumen to deliver measurable impact through intelligent automation and data-driven insight.
What you'll work on
- AI Software Development:
- Design, develop, and maintain production-grade AI/ML code, services, and integrations. Build and iterate on AI prototypes, proofs of concept, and production systems.
- Contribute to codebases, CI/CD pipelines, and engineering standards for AI solutions.
- Collaborate with teams to ensure maintainable, testable, and scalable implementations.
- Design and operate agent-driven systems that autonomously execute workflows, with humans providing oversight, governance, and continuous optimization.
- AI Use Case Development: Partner with business units to identify and evaluate high-value AI opportunities that align with Lightedge’s strategic goals.
- Solution Architecture: Contribute to the design of scalable and cyber-resilient AI and ML solutions, ensuring seamless integration with enterprise systems (e.g., ServiceNow, CRM, ERP, data lake).
- System Integration: Collaborate with data engineering and platform teams to operationalize AI models and embed intelligence into workflows and customer experiences.
- Data Strategy Alignment: Ensure all AI initiatives align with enterprise data governance, security, and privacy standards.
- Innovation Evangelism: Act as a technical and strategic advisor to business stakeholders on how to responsibly leverage emerging AI technologies.
- Cross-Functional Collaboration: Work closely with product management, IT, and Operations, and Security teams to ensure a secure, resilient, consistent delivery, and maintainability.
- Communications: Regularly communicate with executive leadership and business stakeholders to align AI strategy with organizational goals.
- Optimize: Monitor, evaluate, and optimize the performance of deployed AI models and systems.
- Maintain: Own post-deployment support, monitoring, and continuous improvement of AI systems until transitioned to long-term support.
- Cyber Resilience: Ensure that all underlying systems are protected against cybersecurity threats and can recover rapidly in the event of a cyberattack or unexpected system outage.
- Documentation and Governance:Develop and implement AI governance frameworks and ensure ethical AI practices, including maintenance of architectural diagrams, model documentation, and compliance records for AI systems
- Assist Sales: Work with the Sales team as needed, serving as an AI SME during the sales cycle for new customers.
What we're looking for
- Experience integrating AI models into ServiceNow, Salesforce, or similar enterprise platforms.
- Experience building LLM-based applications using frameworks such as LangChain, LlamaIndex, or Semantic Kernel
- Experience developing AI-powered applications such as chatbots, copilots, or intelligent automation tools
- Experience with prompt engineering, evaluation, and tuning of generative AI systems
- Experience with*SQL* and working with large-scale data systems
- Familiarity with embedding models and vector search optimization
- Familiarity with MLOps tools (MLflow, Vertex AI, SageMaker, Databricks, etc.).
- Previous experience leading cross-functional AI or automation initiatives.
- Certifications in cloud architecture or AI engineering (e.g., Azure AI Engineer, AWS Machine Learning Specialty).
- Experience with AI inferencing engines such as vLLM or SGLANG
- Kubernetes Experience.
- Experience with Enterprise LLMs including ChatGPT, Claude, Gemini, and Copilot.
Tags & focus areas
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