Agentic AI Engineer – Connectivity

Role Summary:

Define how AI agents are engineered and deployed across mfgs connected-product ecosystem. As the Connectivity division's hands-on technical lead for agentic AI, the Lead AI Engineer will research emerging technologies, rapidly prototype promising approaches, and translate the best ideas into reusable engineering frameworks, reference architectures, and secure, enterprise-grade production solutions. Working at the intersection of agentic AI, software engineering, IoT, telematics, and connected machines, this role will improve how engineers build, diagnose, support, and operate connected products, partnering with Connectivity, Engineering, Cybersecurity, Enterprise AI, and business stakeholders across the full solution lifecycle, from use-case discovery and architecture through integration, evaluation, deployment, observability, governance, and value realization.

This is not a prompt-engineering-only or research-only role. You will write code, build prototypes, make architectural decisions, and establish engineering patterns that take AI systems from concept to production, while ensuring alignment with enterprise standards for security, compliance, performance, and responsible AI. Applix will equip this role with modern AI engineering tools, including GitHub Copilot and current models and platforms from Anthropic, OpenAI, xAI, and other leading providers, based on enterprise approval and fit for purpose, as well as locally hosted and open models where they offer advantages in data control, latency, reliability, cost, or specialized performance. Experience in software engineering, cloud technologies, generative AI, agent-based architectures, and enterprise-scale solution delivery is essential.

What You Will Do:

• AI Research and Rapid Prototyping: Evaluate emerging LLMs, agent frameworks, protocols, and locally deployable models, and build prototypes using tools such as GitHub Copilot and offerings from Anthropic, OpenAI, xAI, and other leading providers. Assess capability, cost, performance, security, and enterprise readiness to recommend Connectivity’s supported AI engineering stack.
• Agentic Frameworks and Production Engineering: Develop reusable components for agent orchestration, context engineering, RAG, memory, tool use, MCP integration, multi-agent workflows, and human-in-the-loop controls. Design and build secure, maintainable, production-grade AI agents and services in Python, from prototype through deployment and operations.
• Connected Product and System Integration: Apply AI to connected assets, telematics, embedded systems, device lifecycle management, remote services, and engineering workflows, integrating approved enterprise systems, APIs, documentation, and connectivity data while enforcing identity, access, and data-protection requirements.
• Evaluation, Reliability, and Value Measurement: Create evaluation datasets, acceptance criteria, automated tests, tracing, and observability for agent behavior, retrieval quality, latency, and reliability. Measure gains in engineering productivity, troubleshooting, service responsiveness, quality, time, and cost to guide continued investment.
• Technical Leadership and Responsible AI: Serve as Connectivity’s technical point of contact for agentic AI, establishing reference architectures and engineering practices teams can adopt consistently. Partner with enterprise architecture, cybersecurity, privacy, legal, and AI governance teams to ensure secure, auditable, and responsible AI patterns.

Required Qualifications:

• Bachelor’s degree in computer science, engineering, data science, or a related field.
• Strong software engineering experience, including hands-on Python development and API integration.
• Hands-on experience building Generative AI or LLM-based applications using RAG, tool calling, agent orchestration, or similar patterns.
• Experience taking software or AI solutions from prototype through production deployment and support.
• Familiarity with cloud deployment, software testing, source control, and production monitoring.
• Ability to translate business problems into technical solutions and explain results to technical and nontechnical stakeholders.

Considerations for Top Candidates:

• Experience using AI coding assistants and model platforms such as GitHub Copilot, Anthropic Claude, OpenAI models, xAI models, or comparable enterprise AI technologies.
• Experience evaluating, optimizing, or deploying open-weight or locally hosted AI models, including associated inference, security, observability, and lifecycle considerations.
  • Experience with Azure, AWS, or GCP and cloud-native AI services.
  • Knowledge of Docker, Kubernetes, infrastructure-as-code, and platform operations
• Experience with frameworks such as LangGraph, LangChain, Semantic Kernel, CrewAI, or similar technologies.
• Experience with IoT, telematics, embedded systems, connected equipment, or industrial digital platforms.
• Familiarity with connectivity technologies such as MQTT, HTTPS, cellular, satellite, and device-to-cloud communication.