Overview
We are seeking an intermediate-level Generative AI Engineer with experience in developing and implementing solutions based on Large Language Models (LLMs), intelligent agents, and RAG architectures for enterprise environments. The ideal candidate should possess strong skills in Python, API integration, AI agent development, and the use of agentic frameworks (such as LangGraph, CrewAI, AutoGen, and Semantic Kernel) and AI cloud platforms (including Vertex AI, Azure AI, AWS Bedrock, or OpenAI Enterprise).
Your Role and Responsibilities
What You’ll Do
•Service development, automation, API integration, data processing, and scripting.
•Building agents with tools, memory, prompts, task flows, and basic orchestration.
•Implementing connectors for APIs, documents, databases, and workflows.
• Implementing embeddings, vector databases, chunking, semantic search, metadata filtering, and source control.
• Hands-on experience with OpenClaw, LangGraph, CrewAI, AutoGen, Semantic Kernel, or equivalents.
• Practical use of Vertex AI, Azure AI, AWS Bedrock, or OpenAI Enterprise.
• Prompt testing, response evaluation, basic monitoring, traceability, and version control.
• Secure credential handling, basic access control, input validation, prompt injection mitigation, and use of guardrails.
• Queries, data analysis, connecting to corporate sources, and basic understanding of data models.
•Consuming, exposing, and integrating services.
REQUIREMENTS
What This Role Requires
- Advanced Python
- Agent implementation
- MCP / Tool Calling / Function Calling
- Enterprise RAG
- Agentic frameworks
- Cloud for AI
- LLMOps / Basic MLOps
- Applied security
- SQL and data handling
- REST APIs
- Experience delivering in [context — e.g. regulated industries, nearshore teams, AI-integrated systems]
- Strong written and verbal communication in English
Working at Motivus
What Working Here Looks Like
- Guild community: You’re part of the [Data & AI / Backend / Cloud] Guild — a community of practitioners in your domain. Structured learning, peer review, and certifications happen here, not in isolation.
- Outcome accountability: Your work connects directly to a client result. We don’t measure activity — we measure delivery.
- AI in your workflow: Through AI Champions and Guild initiatives, you’ll actively use and shape how AI tools are applied to engineering work — not just hear about it in a townhall.
- Defined career path: Structured progression from individual contributor to technical lead to Guild leadership, with certifications and milestones that are visible, not arbitrary.
