Key skills
AIMLGenerative AILLMOpenAIAnthropicGeminiLangChainLlamaIndexAgenticLangGraphAWSAzureGCPServerlessMentoringCommunication
About this role
MNTN is a company that prioritizes its people and aims to innovate in Connected TV advertising. They are looking for a Software Engineer on the AI Efficiency team to design and build AI-powered tools that enhance efficiency and support business teams across the organization.
Responsibilities:
- Design and build internal AI-powered tools and integrations — including generative AI applications and autonomous AI agents — that help business teams work smarter and faster
- Architect, develop, and maintain AI agents that automate complex multi-step workflows, make decisions, and interact with internal systems on behalf of users
- Evaluate, prototype, and recommend generative AI solutions (LLM APIs, agent frameworks, code generation tools) tailored to specific business problems
- Act as an AI evangelist within engineering — running workshops, writing internal guides, and mentoring teammates on generative AI development patterns and agent design
- Define and maintain best practices for prompt engineering, agent orchestration, model selection, responsible AI usage, and cost management
- Partner with product and business stakeholders to identify high-impact opportunities for generative AI and agent-driven automation
- Stay on the cutting edge of the generative AI and agentic AI landscape and translate new developments into actionable opportunities for the company
Requirements:
- 3+ years of software engineering experience, with at least 1 year focused on generative AI or LLM application development
- Experience with Model Context Protocol (MCP) — building MCP servers, integrating MCP clients, or connecting AI agents to external tools and data sources via MCP
- Hands-on experience building with LLM APIs (e.g., OpenAI, Anthropic, Gemini, open-source models), vector databases, and orchestration/agent frameworks (e.g., LangChain, LangGraph, CrewAI, LlamaIndex, ADK)
- Experience designing or building AI agents — including tool-using agents, multi-agent systems, or agentic workflows
- Cloud-native development experience — comfortable deploying and operating services in AWS, GCP, or Azure using containers, serverless functions, or managed AI/ML services
- Strong communication skills — able to explain complex generative AI concepts to non-technical stakeholders and get engineers excited about new tools
- A product-minded approach: you think about user problems first, not just the technology
- Self-directed and comfortable operating with ambiguity in a fast-moving space