AI Engineer

Sightly is a growing technology company leading the revolution in real-time marketing and brand intelligence. Join us as we pursue our disruptive mission to empower businesses everywhere to make the most authentic + profitable decisions in real time. Our AI-driven Brand Mentality® platform enables brands and agencies to leverage an ever-changing ocean of news, premium publisher, CTV, social, creator, and audience data to make more intelligent decisions at the speed of culture. At Sightly, we’re passionate about our product, our customers, our impact on the world, and most importantly our team. About the role: We're building the cultural intelligence layer for modern media buying and AI is at the center of how we deliver it. As our AI Engineer, you'll own the agent and MCP infrastructure that powers real-time data tools used by strategists, media planners, across media agencies and brands. This is a hands-on engineering role. You'll design and ship agents, extend our MCP server toolset, and work closely with product and data teams to turn raw cultural signals across news and social media into media intelligence and media activation. What you'll work on: Agentic systems that orchestrate multi-step, non-deterministic workflows end-to-end: durable, observable, and reliable in production MCP infrastructure with new tools, long-running background capabilities, and interactive surfaces that bring our intelligence directly into client agent environments Data-to-activation pipeline that turns large-scale cultural and media signals into intelligence strategists and planners can act on Integration of major media and advertising platforms to move seamlessly from insight to execution to measurement Harden production agent infrastructure: evals, tracing, prompt and model management, cost controls, and guardrails for systems where outputs are probabilistic Work across a Python, FastAPI, FastMCP, Temporal, PostgreSQL, and GCP stack, partnering closely with product, data, and design to shape how AI shows up for our users Core Competencies: Agent Engineering Experience building production agents, with real tool use, state management, and failure handling Familiarity with at least one agent framework (Agno, LangGraph, CrewAI, or similar) Strong understanding of multi-step reasoning, tool chaining, and when to use which pattern Ability to write and iterate on system prompts that produce consistent, structured, schema-conformant outputs Good judgment on when to use an agent vs. a deterministic workflow vs. a single LLM call MCP & Tool Development Hands-on experience building or consuming MCP servers (Model Context Protocol) Understanding of tool schema design including parameter validation, error contracts, and what makes a tool LLM-friendly vs. brittle Comfortable debugging tool call failures from the model side (hallucinated parameters, invalid combinations, timeout patterns) LLM Proficiency Deep familiarity with at least one frontier model family Understanding of context windows, prompt caching, extended thinking, and cost/latency tradeoffs Practical understanding of model behavior: failure modes, sampling temperature, structured output reliability Evals & Quality Experience designing evals for non-deterministic systems: regression suites, LLM-as-judge, golden datasets Comfort instrumenting agents with tracing (Langfuse, LangSmith, or similar) and using traces to diagnose silent failures Knows the difference between "the prompt works on my machine" and "the agent is production-ready" Backend & Infrastructure Python, FastAPI, FastMCP, async/await patterns PostgreSQL, SQLAlchemy, Alembic GCP (Cloud Run, Cloud SQL, GCS, Secret Manager) or equivalent cloud-native experience Docker, CI/CD pipelines (GitLab CI a plus) Software Engineering Fundamentals Clean architecture habits, SOLID principles Strong typing discipline (Pydantic models, type annotations) Test-driven mindset: pytest, mocking external dependencies, covering edge cases not just happy paths Nice to have: Experience with Temporal or other workflow orchestration tools TypeScript / React familiarity (our frontend is React 19 + assistant-ui) Media, adtech, or data intelligence domain knowledge Experience designing agent outputs that feed into programmatic or social media buying workflows Location: This role is remote as we are a 100% distributed company