AI Engineer

Position Summary 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. \n

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 m aintenance 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. \n