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)

Additional requirements:

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
Experience: 5+ years in AI engineering or related roles

WILL BE A PLUS

Principal AI Engineer