Senior AI Engineer

TaskRay•Denver, CO

About The Position

First impressions matter. TaskRay is on a mission to ensure businesses get off to a great start with a flawless customer experience once the opportunity is marked Closed Won. TaskRay is the leader in post-sale work management in the Salesforce ecosystem. We help companies make the transition from sales to customer success frictionless, driving greater efficiencies and creating unmatched customer experiences – all within Salesforce. Culture is at the heart of TaskRay. We look for team members who share our values and bring them to life in their own ways. If these resonate with you, TaskRay could be the place for you: Connection. Cultivate authentic connections with customers, partners, and each other. We believe in a customer-first mindset across everything we do, continuously learning and focusing on the ‘We’ rather than the ‘I’. Integrity. We are transparent, sharing information and feedback freely. We infuse every action with honesty, fairness, and respect for customers and colleagues alike. We do the right thing, even when no one is watching. Hunger. We have high expectations and expect high performance of ourselves and our teams. We constantly look to innovate and improve, unleashing our creativity and persistence to find solutions and deliver exceptional results. Thrive. We love what we do and bring our best selves to work every day. We show up as real human beings who speak from the heart, value others’ contributions and invest in each other’s growth. The Role We are hiring a Senior AI Engineer to build production agent capabilities for TaskRay's AI platform. You will work closely with our Staff AI Engineer and our product team to ship features that customers actually use, against the most demanding bar in our company: agents that automate real implementation work for enterprise onboarding teams. You will work across the full AI stack: prompt and agent design, retrieval, evals, tool integrations, and the off-platform service layer that hosts it all. You will partner with design partner customers to learn what the agent needs to do before generalizing it into the product. We are sequencing these agents deliberately, starting with internal-facing capabilities (PM Agent and Execution Agent) before extending to the External Onboarding Agent. The MCP server is the foundation that underpins all three. This is a heads-down builder role with leverage. We are small, the work is real, and shipping speed matters. Note We are hiring AI engineers, not Salesforce engineers. You bring the agent craft (production LLM systems, evals, retrieval, tool use, multi-step reasoning). We will teach you what you need to know about the Salesforce platform our product is built on. The integration work is real but it is not the center of the role; the agents are.

Requirements

You have shipped LLM-based features to production users. You have lived through the gap between a working demo and a system that actually serves customers
Hands-on experience with retrieval pipelines, agent loops with tool use, and the evals that keep them honest
Strong Python proficiency (the lingua franca of the AI stack), with production experience deploying real systems
Hands-on experience integrating LLM provider APIs (Anthropic Claude, OpenAI, or equivalent) into real applications, including evals, prompt iteration, and cost and latency engineering
Strong written communication. You can explain technical decisions clearly to product, customer success, and customers themselves.
We are level-agnostic on years; we care about what you have shipped.

Nice To Haves

Experience with Model Context Protocol (MCP) or agent orchestration tooling
Curiosity about the Salesforce platform. You do not need to know it. We will teach you. But you should be excited about integrating agents with the system of record where most enterprise customer-facing work actually happens.
Experience with compound or agentic coding workflows (Claude Code, Cursor, or equivalent)
Comfort in customer-facing technical contexts, including design partner work and customer demos (all virtual)

Responsibilities

AI Feature Development: Build production agent capabilities for our PM Agent (the internal-facing agent that maintains live project status and increases customer team productivity), our Execution Agent (the internal-facing agent that completes tasks on behalf of human counterparts), and our External Onboarding Agent (the customer-facing agent that handles status, document collection, and end-customer interaction).
Contribute to our MCP server, the platform layer that connects these agents to TaskRay's project, customer, and onboarding data
Design prompts, agent loops, and tool integrations that meet customer-grade reliability, not demo-grade
Own subsystems end to end: retrieval pipelines, eval harnesses, prompt libraries, tool registries, and the observability around them
Design Partner Work: Partner with design partner customers to ship custom agent workflows that teach us what to productize. Engagements run via video and shared tooling, not on-site
Translate customer-specific work into reusable building blocks for the core platform
Partner with Product on customer-facing demos, walkthroughs, and feedback loops
Platform and Integration: Help build and maintain the MCP server and the off-platform service layer that hosts our agent capabilities
Develop integrations with adjacent systems where the agents need them (Google Drive, calendar and meeting transcripts, document stores, CRM data)
Contribute to the eval, observability, and reliability infrastructure our Staff engineer is establishing
Engineering Craft: Write clean, well-documented code that your teammates will thank you for
Participate in code reviews with a constructive, growth-oriented mindset
Actively contribute to our agentic coding standards and norms