Forward Deployed AI Engineer

What you’ll do: You will play a lead technical role in designing and delivering AI-enabled solutions across the enterprise.

Build and deliver AI solutions: Design, build, test, and deploy AI-enabled applications, services, and workflows Work with LLMs, intelligent agents, and automation frameworks to solve real business problems Take solutions from prototype to production, ensuring they are reliable and scalable

Own technical design: Lead architecture and design for:LLM integrations Retrieval-augmented generation (RAG) Agent workflows and orchestration API and enterprise system integrations Ensure solutions are secure, reusable, and aligned with enterprise standards

Drive engineering standards: Define and apply reusable patterns and best practices for AI delivery Improve how teams build, deploy, and scale AI solutions Contribute to responsible and governed AI adoption

Support production and continuous improvement: Ensure solutions are production-ready (testing, monitoring, observability) Troubleshoot issues, perform root cause analysis, and continuously improve systems Optimize for performance, cost, reliability, and user experience

Partner across teams: Work closely with product, architecture, platform, security, and business stakeholders Translate business needs into clear technical solutions and delivery plans Influence decisions through technical expertise, not authority

What you bring: Engineering foundation

Strong experience building scalable, distributed systems

Deep knowledge of: APIs, microservices, and service-based architectures Cloud-native development (Azure preferred) CI/CD, containerization, and deployment automation Experience with event-driven systems, data pipelines and data platforms.

AI / GenAI expertise: Hands-on experience building LLM-powered applications in production

Strong Experience with: Prompt design and evaluation Model limitations (hallucination, variability, context constraints) Agent design and orchestration workflows Tool/API integrations RAG and knowledge grounding patterns

Delivery and operational mindset: Experience across the full lifecycle: Use case definition Solution design Integration Deployment Monitoring and optimization

Strong understanding of: AI observability (quality, latency, cost) Reliability and system performance

Risk, security, and governance awareness: Experience working in regulated environments

Strong awareness of: Data privacy and security AI governance and controls Misuse prevention (incl. prompt injection risks) Auditability and human-in-the-loop safeguards