Forward Deployed AI Engineer

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