Forward Deployed Engineer
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Job Description
Forward Deployed Engineer embedding with product teams to build production AI services for IFS enterprise software. Connecting LLM, RAG, agentic, and cloud-native capabilities to business workflows.
Responsibilities:
- Embed directly with product teams to understand business flows and identify where AI can help
- Lead technical discovery and distinguish business-process bottlenecks from symptoms
- Identify opportunities to apply or extend existing AI services
- Help product teams design and build AI-first applications
- Gather and document requirements for new AI services
- Partner with engineering teams to scope, design, and build new AI services
- Work hands-on with data pipelines, model training and serving, MLOps tooling, and product integrations
- Ship initial versions quickly and harden them with monitoring, feedback loops, and production-grade reliability
- Translate product pain points and business requirements into input for the AI services roadmap
- Collaborate with product owners, business stakeholders, and engineering leads
- Communicate technical trade-offs clearly to non-technical audiences
- Connect IFS R&D’s AI capabilities with product teams and provide field insights for future development
Requirements:
- 5+ years of software engineering experience
- Strong hands-on capability building and deploying production systems
- Strong Python skills and willingness to work across other languages and existing customer codebases
- Experience building and operating cloud-native services
- Azure experience, particularly AKS, Blob Storage, Key Vault, and Azure-hosted AI/model services
- Experience integrating LLMs and AI services into production applications, including model APIs, authentication, gateways, reliability, latency, cost, and observability
- Experience building agentic applications using tools, APIs, and orchestration frameworks
- Hands-on MCP experience strongly preferred
- Hands-on experience designing production RAG and retrieval systems, including embeddings, vector databases, indexing, retrieval quality, and evaluation
- Strong experience with REST APIs, authentication/authorization, external systems, data contracts, and complex integrations
- Experience with Docker, Kubernetes, Helm, GitOps/ArgoCD, and Terraform or equivalent infrastructure-as-code tooling
- Comfortable working with relational, document, vector, and object stores
- Understanding of event-driven and distributed systems architecture
- Understanding of AI evaluation, observability, monitoring, failure modes, and quality/latency/reliability/cost trade-offs
- Experience building and owning production ML/data systems, not just notebooks or prototypes
- Ability to turn ambiguous business problems into scoped, shipped systems
- Ability to communicate technical concepts and trade-offs clearly to non-technical stakeholders
- Experience working in or alongside platform or infrastructure teams
- Ability to work directly with customers and stakeholders, rapidly prototype, and productionize solutions
- Demonstrable hands-on experience designing, building, and shipping production AI applications
Benefits:
- Flexible and hybrid work opportunities
- Inclusive workplace experiences
- Sustainability-focused company environment
- Global, diverse work environment
- Opportunity to work with AI-driven enterprise software and make a worldwide impact
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