General Description:
We are looking for AI-first engineers who use Generative AI as a foundation of software engineering. This role applies AI across the Product Development Lifecycle, from requirements and design to deployment and operations. You will design and build AI Agents, apply prompting techniques, and implement architectures like RAG. You will collaborate closely with product and engineering teams to embed AI into daily workflows. The ideal candidate uses AI IDEs regularly and has solid knowledge of MCP and related Agentic AI concepts.

Responsibilities:
Translate product and engineering challenges into AI solutions
Build and deploy AI Agents with reasoning and memory integration
Apply prompt engineering and RAG to real-world use cases
Collaborate with teams to embed AI into requirements, coding, testing, and operations
Prototype and test AI solutions, balancing experimentation with production readiness
Support demonstrations of AI use cases for clients and internal stakeholders
Stay current with advances in Generative AI and bring them into practice
Communicate findings and trade-offs clearly in English to both technical and business audiences
Required qualifications:
Bachelor's Degree in Computer Science, Engineering, Applied Math, or related fields (or equivalent experience)
Strong programming background in Python (or similar) with experience in Generative AI frameworks and APIs
Daily use of Generative AI IDEs or environments
Proven experience in Prompt Engineering
Solid understanding of RAG, MCP (Model Context Protocol), A2A (Agent to Agent) and ACP (Agent Coordination Protocol)
Exposure to deploying AI-powered solutions across stages of the product lifecycle
Strong technical communication skills in English
Exposure to cloud native environments
Ability to work independently and collaboratively in fast-paced environments

Desired qualifications:
Knowledge of how to integrate short and long-term memory in agents
Exposure to Agentic AI frameworks, autonomous agents, or multi-agent orchestration
Knowledge of Knowledge Graphs approaches
Awareness of monitoring AI systems for evals validation, performance, accuracy, and cost monitoring

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