Software Engineer, AI Solutions

As a Software Engineer on the AI Efficiency team, you will bridge the gap between emerging AI capabilities and real-world business needs. You’ll design and build AI-powered tools and agents that automate workflows, support business teams, and unlock new opportunities for efficiency across the organization. Your work will directly influence how AI drives impact throughout MNTN.

• →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.

• Delivery of AI tools and agents that measurably improve efficiency across business teams.
• Successful deployment of generative AI solutions with high adoption and usability within internal teams.
• Clear documentation, guides, and workshops that enable other engineers and business stakeholders to leverage AI tools effectively.
• Adoption of best practices for responsible AI usage, cost management, and agent orchestration across the organization.
• Influence on business outcomes through AI-driven automation and insights.

• 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.

Our recruiters will always reach out using an email address ending with @mountain.com OR @mntn.com. If you’re contacted by someone without that address and they mention a Reference Code (which we never use), then that ain’t us folks. Tell those trolls to take a hike–you’re waiting to climb a MNTN.

MNTN provides advertising software for brands to reach their audience across Connected TV, web, and mobile. MNTN Performance TV has redefined what it means to advertise on television, transforming Connected TV into a direct-response, performance marketing channel. Our web retargeting has been leveraged by thousands of top brands for over a decade, driving billions of dollars in revenue.

Our solutions give advertisers total transparency and complete control over their campaigns all with the fastest go-live in the industry. As a result, thousands of top brands have partnered with MNTN, including Tarte, Decked, and National University.

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