Job Description

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.

Key Responsibilities

• 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.

Required Qualifications

• Proficiency in Python for AI/ML and data processing
• Experience with one or more of JavaScript/TypeScript, Java, or C# for API and application development.
• Experience with building microservices, distributed systems, CI/CD Pipeline, and DevOps practices.
• Experience evaluating AI model outputs, including prompt testing, benchmarking, and performance tuning
• Knowledge of vector databases, RAG pipelines, or LLM orchestration frameworks.
• Bachelor’s or Master’s degree in Computer Science, Data Science, Engineering, or equivalent experience.
• 3+ years of experience working with machine learning, NLP, or generative AI technologies, including RAG pipelines, vector databases, and prompt engineering
• Strong proficiency in cloud platforms (Azure, AWS, or GCP) and API-based integrations.
• Experience deploying models via REST APIs, data pipelines, or event-driven frameworks.
• Working knowledge of data governance, model monitoring, and security principles.
• Excellent communication skills and ability to translate technical concepts into business value.

Preferred Qualifications

• 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.

Success Metrics

• Increased AI-enabled automation efficiency and accuracy across business processes.
• Reduction in manual data handling through intelligent integrations.
• Timely delivery of production-ready AI capabilities.
• Cross-departmental adoption of AI solutions.
• Measurable business ROI from deployed AI initiatives.