About the Role
We are looking for a highly skilled and hands‐on AI Architect to design and lead the implementation of multi‐agent (agentic) systems in enterprise environments.
This is not a prompt engineering or chatbot role.
We are focused on building production‐grade AI systems, where multiple agents collaborate, reason, and execute complex workflows integrated with real business processes.
Key Responsibilities
Design multi‐agent architectures (task decomposition, orchestration, coordination patterns).
Define how LLM‐powered agents interact with enterprise data platforms, APIs and tools, operational workflows.
Lead agentic systems from PoC to production with model, cost, security, privacy, and responsible AI guardrails.
Establish observability, tracing, feedback loops, and controls for agent behavior.
Define memory strategies (short‐term, long‐term, contextual grounding).
Collaborate with data, platform, and engineering teams to integrate AI into core systems.
Guide and train teams on best practices for scalable and reliable AI systems.
Qualifications
What we're looking for
Strong background in software architecture and distributed systems.
Hands‐on experience building complex LLM‐based applications.
Experience designing complex workflows or orchestration systems.
Solid understanding of RAG architectures, retrieval optimization, and retrieval quality.
Strong understanding of LLM fundamentals and transformer architecture.
Solid grasp of transformer attention, embeddings, tokenization.
Understanding of LLM training paradigms (pretraining vs fine‐tuning vs instruction tuning).
Knowledge of context windows, limitations, hallucinations and mitigation strategies.




Familiarity with NLP concepts such as semantic similarity, text embeddings, vector representations.
Information retrieval principles and ability to reason about model behavior and limitations.
Experience with multi‐agent frameworks (LangChain, Semantic Kernel, Agent Framework, CrewAI, or custom).
Experience on platforms for managing the lifecycle of generative AI applications and agents like Amazon Bedrock, Google Vertex AI, or Azure AI Foundry.
Familiarity with protocols such as MCP, UCP, A2A, AP2, tool use/function calling, agent coordination patterns, memory and context management.
Experience evaluating and improving LLM reliability and accuracy.
Cloud experience (AWS, Azure, or GCP).
Strong coding skills (Python preferred).
Experience integrating AI with enterprise systems (APIs, data platforms, event‐driven systems).
What Makes This Role Different
Focus on systems, not isolated models.
Real enterprise use cases, not experimental demos.
Opportunity to define architecture patterns for agentic systems.
Work at the intersection of AI, data, and distributed systems.
Additional Information
We offer a competitive salary package, share plan, performance bonuses, and a range of benefits including career development, learning opportunities, work‐life balance, health programmes, and community initiatives.
At Endava, we're committed to creating an open, inclusive, and respectful environment where everyone feels safe, valued, and empowered.
We welcome applications from people of all backgrounds, experiences, and perspectives.
Hiring decisions are based on merit, skills, qualifications, and potential.
If you need adjustments or support during the recruitment process, please let us know.
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📌 Ai Architect – Agentic Systems (Llm & Multi-Agent Solutions) (Medellín)
🏢 Endava
📍 Medellín