Generative AI Engineer

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L-com Global Connectivity

WebsiteLinkedInAll Job Openings

201 - 500 employees

Founded 1982

🏥 Healthcare

📦 Logistics

🏗️ Construction

💰 Private Equity Round on 2006-03

Healthcare • Logistics • Construction

L-com Global Connectivity is a leading provider of wired, wireless, and industrial connectivity products. They specialize in custom cable assemblies and offer a wide range of connectivity solutions including adapters, antennas, and enclosures. With a commitment to fast delivery and high-quality products, L-com serves various industries including telecommunications, industrial automation, and healthcare.

📋 Description

• Design and build production-grade generative AI systems - agentic workflows, multi-step RAG pipelines, and LLM-powered applications integrated with enterprise data and services • Define and implement reusable engineering patterns for prompt management, workflow versioning, structured outputs, tool orchestration, and rollback across production AI services • Apply judgment around model selection and routing, token and latency optimization, cost management, and the appropriate boundaries between AI-driven and deterministic application logic • Continuously evaluate emerging AI models, tools, and architectural approaches, incorporating improvements into existing systems incrementally • Integrate AI systems with enterprise data sources, internal APIs, and platforms to enable reliable, production-ready workflows • Own operational outcomes for production AI systems - reliability, latency, throughput, cost efficiency, and scalability targets • Implement and maintain monitoring, observability, tracing, and alerting frameworks to ensure operational visibility and rapid issue resolution • Design and maintain CI/CD pipelines for deployment, versioning, and release management of AI services • Lead production incident response and root cause analysis, driving systemic improvements that reduce recurrence • Build and maintain automated evaluation pipelines for LLM outputs - prompt regression testing, retrieval quality validation, and failure mode tracking • Implement human-in-the-loop controls, content guardrails, schema validation, and structured output enforcement to ensure trusted and auditable AI outputs • Securing AI systems against prompt injection, data leakage, and unauthorized access, aligning with enterprise compliance and security standards • Define and enforce engineering standards, patterns, and best practices across all GenAI workstreams • Make and defend architectural decisions with clarity, providing the technical rationale needed for the Manager and stakeholders to align and move forward confidently • Work closely with the Manager, GenAI Engineering to receive, refine, and execute on scoped GenAI work - contributing technical judgment to prioritization and tradeoff decisions • Provide hands-on code review and technical guidance to engineers contributing to GenAI workstreams, raising overall quality through direct feedback and demonstration

🎯 Requirements

• Demonstrated experience shipping production-grade LLM or generative AI systems - prompt and workflow design tradeoffs, model selection and routing decisions, tool use and agent orchestration boundaries, and the distinction between AI guardrails and deterministic application logic • Experience building automated evaluation pipelines for LLM outputs, including gold set construction, model-based evaluation approaches, prompt regression testing, retrieval quality validation, and failure mode analysis across the full LLM application stack • Experience implementing human-in-the-loop controls, content guardrails, and schema-based output validation for enterprise AI deployments • Strong track record designing, building, and operating complex distributed systems in enterprise production environments, with clear ownership of reliability, performance, and operational outcomes • Experience with CI/CD pipeline design and operation for AI services - including deployment strategies, versioning, and release management in production environments • Proven ability to define and enforce GenAI engineering standards, patterns, and best practices across a cross-functional team • Experience designing and operating cloud-native APIs, microservices, and event-driven architectures on Azure or equivalent cloud platform • Experience integrating AI systems with enterprise data sources, internal APIs, and security controls in compliance-sensitive environments • Demonstrated track record of shipping production AI systems iteratively - with regular release cadence, feedback incorporation, and continuous improvement • Bachelor's degree in Computer Science, Engineering, Data Science, or related field, or equivalent practical experience