We’re looking for a hands-on AI Engineer with experience building and scaling GenAI solutions, with a focus on LLM-based systems, Agent frameworks, and Google Cloud Platform (Vertex AI, Cloud Run Functions, BigQuery, etc.). This role sits at the intersection of engineering, data science and applied AI. You will contribute to system architecture, design and deploy agentic workflows, and collaborate cross functionally to bring business-use cases to life.

Must-Have Skills & Experience• 4-7 years of experience in software engineering, ML engineering, or AI product development.

• Understanding of Prompt Engineering, tool calling, RAG evaluation, and LLM tuning.
• Exposure to MLOps or AgentOps workflows.
• Demonstrated expertise building LLM-powered applications (eg. RAG pipelines, custom agents, conversational assistants).
• Hands-on experience with Google Cloud Platform, especially Vertex AI, BigQuery, Cloud Run Functions, IAM, Cloud Storage.
• Familiarity with Agent Frameworks like Agent SDK, LangGraph, CrewAI, PydanticAI, LlamaIndex, or LangChain.
• Strong programming skills in Python, with ability to design and maintain modular and scalable codebases.
• Comfortable in ambiguity and iterative design with a proven ability to “fail fast,” test quickly, and learn continuously.
• Excellent communication skills- can interface with clients, contribute to architectural decisions, and collaborate with cross-functional teams.
• • Comfortable explaining technical concepts to non-technical stakeholders, such as project managers, operations leads, or C-level execs.
• Skilled at running or contributing to client demos, working sessions, or showcasing PoCs.
• Confidence in discussing trade-offs (eg. cost vs latency, open-source vs proprietary, GenAI vs rules-based).
• Experience in requirements discovery - gathering business pain points and translating them into AI or data workflows.
• Ability to collaborate on project documentation, training materials, or client handoff guides

• Design, build, and deploy LLM-powered AI solutions using frameworks such as LangGraph, Agents SDK (Google, Microsoft or Open AI), or PydanticAI.
• Architect and implement multi-agent workflows, including hybrid systems integrating search, automation, retrieval, and summarization.
• Work hands-on with Vertex AI, Cloud Run Functions, BigQuery, and other GCP services to build scalable backends for AI agents.
• Collaborate cross-functionally with Solution Architects, Functional Consultants, and Data Engineers to translate business problems into technical solutions.
• Design, monitor, and improve agent reliability, response quality, and hallucination mitigation strategies.
• Prototype rapidly, evaluate LLMs (open-source and proprietary), and deploy PoCs with measurable outcomes.
• Write robust, maintainable code, with clear documentation, testing, and version control.
• Support internal accelerators and ideate client enablement by defining Fastloop’s agentic platform toolkit.
• Stay up to date with trends in LLMs, vector databases, orchestrators, and GCP innovations. Bringing insights into client work and internal enablement.

Must-Have Skills & Experience• 4-7 years of experience in software engineering, ML engineering, or AI product development.

• Understanding of Prompt Engineering, tool calling, RAG evaluation, and LLM tuning.
• Exposure to MLOps or AgentOps workflows.
• Demonstrated expertise building LLM-powered applications (eg. RAG pipelines, custom agents, conversational assistants).
• Hands-on experience with Google Cloud Platform, especially Vertex AI, BigQuery, Cloud Run Functions, IAM, Cloud Storage.
• Familiarity with Agent Frameworks like Agent SDK, LangGraph, CrewAI, PydanticAI, LlamaIndex, or LangChain.
• Strong programming skills in Python, with ability to design and maintain modular and scalable codebases.
• Comfortable in ambiguity and iterative design with a proven ability to “fail fast,” test quickly, and learn continuously.
• Excellent communication skills- can interface with clients, contribute to architectural decisions, and collaborate with cross-functional teams.
• • Comfortable explaining technical concepts to non-technical stakeholders, such as project managers, operations leads, or C-level execs.
• Skilled at running or contributing to client demos, working sessions, or showcasing PoCs.
• Confidence in discussing trade-offs (eg. cost vs latency, open-source vs proprietary, GenAI vs rules-based).
• Experience in requirements discovery - gathering business pain points and translating them into AI or data workflows.
• Ability to collaborate on project documentation, training materials, or client handoff guides.