Responsibilities Consult clients during presales to assess AI readiness, constraints, and success criteria, translating visions into actionable requirements Prepare and present presales deliverables including architecture diagrams, assumptions, risks, estimates, scalability considerations, and implementation roadmaps Define AI use cases and agentic scenarios based on client needs Select and justify LLMs per use case based on requirements, cost, latency, safety, capability Architect multi-agent frameworks with orchestrator agents and MCP-style protocols Design and implement single-agent/multi-agent AI systems with defined roles, tool access, memory, safety boundaries Build orchestration logic (routing, delegation, retries, fallback strategies, consensus, human-in-the-loop flows) Develop RAG pipelines (data ingestion, chunking, embeddings, vector databases, hybrid retrieval, relevance optimization) Implement learning & feedback loops for continuous agent improvement Design custom or adapted models (prompt-tuned agents, LoRA fine-tuning, domain-inherited models) Requirements 5+ years in AI engineering or related roles Designing and building AI-powered systems in production Agentic frameworks, multi-agent collaboration, orchestrator/worker models RAG pipelines, relevance tuning Python and/or TypeScript, API design, microservices, cloud-native architectures Practical knowledge of multiple LLM providers (OpenAI, Anthropic, open-source) Ability to build/adapt models (prompt-tuned agents, LoRA fine-tuning, inheritance from foundation models) Knowledge of AWS Bedrock, Azure OpenAI, GCP Vertex AI Understanding governance, security, data residency, pricing models, enterprise integration for cloud AI platforms Production-grade mindset: observability, logging, security, PII handling, cost-efficiency Strong communication skills for explaining AI concepts to non-technical stakeholders English level: Upper-Intermediate #J-18808-Ljbffr
