> Markdown version of [/jobs/ext/731569-ai-ml-engineer-mcp](https://www.wearedevelopers.com/jobs/ext/731569-ai-ml-engineer-mcp). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # AI/ML engineer - MCP - **Company:** ActiveSoft, Inc - **Location:** United States (Remote available) - **Experience:** Expert - **Contract:** Permanent contract - **Skills:** Artificial Intelligence, Computer Vision, Software Engineering, AI Infrastructure, Large Language Models, Multi-Agent Systems, Production Code - **Published:** June 29, 2026 - **Apply:** https://www.dice.com/job-detail/8ff5c940-ca17-4405-b382-f46eb2db6950 ## About the Role * Strong software engineering. You write clean, production code and own systems end to end. * Hands-on fluency with LLMs and agentic systems, including how they fail and how to catch it, with strong opinions about context management. * Experience with MCP servers, agent tooling, or LLM evaluation, or the ability to ramp on them fast. * A researcher''''s instinct paired with a builder''''s bias. You can scope a spike, learn fast, and ship the result. * A measurement mindset. You do not call something done until you can show it works. What we are really looking for The ideal candidate has hands-on experience with some combination of: * Building MCP servers, MCP tools, agent tooling, or similar context/tool orchestration systems * Designing and running LLM/agent evaluations * Building production AI/ML or LLM-powered software, not just prototypes * Working with messy data and turning it into useful, measurable product capabilities * Strong context-management instincts: knowing how LLM systems fail, how to structure inputs/tools, and how to evaluate output quality * Customer/product-facing judgment - ability to understand a customer problem, make sense of loose requirements, and help shape the right solution MCP experience is highly preferred, especially candidates who have built MCP servers and maintained them over time. However, strong candidates with adjacent experience in agent tooling, LLM evals, tool calling, retrieval/context systems, or applied AI infrastructure may also be worth exploring. Must-have profile Strong candidates will likely be: * Senior-level engineers with strong production software engineering fundamentals * Comfortable owning systems end to end * Experienced with LLMs, agents, evals, MCP, tool use, or context-management systems * Comfortable working without perfectly defined specs * Able to translate ambiguous customer/business needs into technical direction * Measurement-oriented - they should care deeply about whether the system actually works * Curious, pragmatic, and biased toward shipping Nice-to-have / differentiators Prioritize candidates who also bring: * Experience building MCP servers and the underlying tools behind them * Experience creating evaluation frameworks for AI/LLM/agentic systems * Experience with computer vision, video, creative analysis, ad tech, marketing tech, or performance marketing data * Experience working directly with product, customers, sales, or solutions teams * Ability to explain prior work clearly and go deep on technical decisions * Startup or small-team experience where requirements were ambiguous and pace was fast ## Related Videos - [Designing the Future of Human<>Agent Collaboration](https://www.wearedevelopers.com/videos/1447-designing-the-future-of-human-agent-collaboration) - [API, MCP or MCP App? 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