AI Engineer - Agentic Workflows

Astoria AI is an early-stage startup building a human-centered talent intelligence platform powered by AI. Our mission is to help people better understand their potential and help organizations attract, develop, and retain high-quality talent.

We are looking for an AI Engineer - Agentic Workflows to help build the intelligence layer behind Astoria AI. This role combines applied LLM engineering, agentic workflow design, ML-informed data pipelines, and product intelligence systems.

You will build AI workflows that gather context from multiple sources, structure messy information, reason over it, call tools, produce auditable outputs, and improve through evaluation and feedback loops.

Compensation: early-stage compensation structure with meaningful equity and salary introduced after MVP/funding milestones.

Location: remote-friendly, async-first collaboration.

What You’ll Do

• Design and build LLM-powered workflows that perform multi-step tasks, not just answer questions.
• Build agentic systems that can retrieve context, call tools, use structured outputs, work with data, and escalate to humans when needed.
• Build data and intelligence pipelines that collect, normalize, classify, enrich, and score talent, company, role, and market signals.
• Work with documents, structured data, product data, APIs, internal knowledge sources, and external signals.
• Apply lightweight ML and statistical approaches for classification, ranking, entity matching, deduplication, confidence scoring, and signal quality measurement.
• Use RAG and retrieval where helpful, with attention to grounding, source traceability, retrieval quality, and evaluation.
• Implement structured prompting, function/tool calling, JSON/schema-based outputs, workflow state, retries, fallbacks, and human-in-the-loop review.
• Build evaluation loops to test output quality, consistency, hallucination risk, retrieval quality, and workflow reliability.
• Collaborate with full-stack engineers to integrate AI workflows into product experiences.
• Monitor and improve cost, latency, observability, reliability, and user trust.
• Prototype quickly, then turn successful prototypes into maintainable product systems.

What You Bring

• Experience building with LLM APIs and modern AI application patterns.
• Strong understanding of agentic workflows: tool use, structured outputs, and multi-step task orchestration.
• Practical experience in ML pipelines such as embeddings, similarity search, classification, evaluation metrics, and data quality measurement.
• Experience with RAG, embeddings, vector databases, or retrieval systems - but not only as a basic chatbot pattern.
• Ability to design workflows with state, constraints, fallback paths, human review points, and observability.
• Ability to evaluate AI outputs using examples, rubrics, regression tests, human feedback, output validation, or automated checks.
• 3-5 years of professional software engineering experience in Python, TypeScript, APIs, databases, and backend application logic.
• Comfort building AI systems that connect to product workflows, internal tools, data sources, and external services.
• Product judgment: you care whether AI is useful, understandable, reliable, and trustworthy to users.
• Clear communication and comfort working in an early-stage, async-first startup environment.

Strong Signals

We are especially interested in candidates who have built AI systems where:

• The LLM calls tools or APIs to complete a task.
• The workflow has multiple steps, not one prompt-response interaction.
• The system uses structured data, schemas, or validated outputs.
• The pipeline collects, cleans, classifies, ranks, or scores real-world data.
• Outputs are traceable, reviewable, and measurable.
• The system handles uncertainty, missing context, or failure cases.
• There is an evaluation or feedback loop.
• A human can review, approve, or correct important outputs.
• The AI feature was used inside a real product, internal tool, customer workflow, or production-like environment.

Nice to Have

• Experience with agent frameworks, workflow orchestration, MCP/tool-use patterns, or multi-model systems.
• Experience building AI copilots, research agents, workflow agents, document agents, or task automation systems.
• Experience with evaluation frameworks, observability, prompt/version management, or AI quality monitoring.
• Experience with entity resolution, knowledge graphs, search/ranking, data enrichment, or intelligence pipelines.
• Experience with hiring, talent, HR tech, recruiting, marketplace, or workflow-heavy SaaS products.
• Prior startup, open-source, side project, or internal tool experience that shows end-to-end AI product building.

What This Role Is Not

This is not primarily:

• A foundation model training role
• A research scientist role
• A prompt-only role
• A basic RAG chatbot role
• A notebook demo role

We are looking for someone who can apply LLMs, retrieval, lightweight ML, data pipelines, and agentic workflow design to build reliable product intelligence.

Why Join Us

You will help define how Astoria AI builds agentic product experiences from the ground up. Your work will directly shape how our system gathers intelligence, reasons over talent context, supports hiring decisions, and makes AI outputs more useful, explainable, and trustworthy.

We value:

• Practical AI over hype
• Reliable workflows over demos
• Product usefulness over technical novelty
• Strong data quality over shallow automation
• Fast learning with engineering judgment
• Human-centered AI design

If you are excited about building AI systems that can reason, use tools, learn from data, and help people make better talent decisions, we would love to connect.