AI Engineer

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 Qualificiations:
• 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.