We are looking for a Forward Deployed AI Engineer who can bridge the gap between business strategy and real-world AI delivery.
This role is about more than building models—it’s about taking AI from idea to production, integrating it into core systems, and ensuring it delivers measurable business value. You will combine hands-on engineering expertise with practical AI implementation, helping teams adopt AI in a way that is scalable, secure and usable.
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
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
