Artificial Intelligence Engineer (Python+ Gen AI)

BeaconFire Inc.; New York (NY); On-site; USD 90,000 - 130,000; Full time

Job summary

BeaconFire Inc. is seeking a talented agentic AI developer in New York, NY, specializing in Vertex AI–based solutions. The role involves building RAG systems, agent orchestration, and designing reliable, tool-using agents. Candidates should have solid Python proficiency and experience with frameworks like Flask or Django. Responsibilities include developing web applications, code reviews, and implementing evaluation metrics, with opportunities to stay at the forefront of technology advancements.

Qualifications

• Decent understanding of the software development/testing life cycle.
• Solid knowledge of vector search concepts and at least one vector DB in production.
• Strong engineering practices: code reviews, testing, telemetry, secure-by-design.

Responsibilities

• Collaborate with the team to design and develop high quality Web applications.
• Design and implement RAG pipelines on Google Cloud / Vertex AI.
• Ship to production: APIs, monitoring/observability, cost/performance optimization.

Skills

Python web application development

Vector databases

Graph databases

RAG solutions development

Version control (Git)

Education

Master’s Degree in Computer Science or related field

Tools

Flask

Django

Google Cloud

Job description

BeaconFire is based in Central NJ, specializing in Software Development, Web Development, and Business Intelligence.

About the Role

Looking for a strong agentic AI developer who can build and productionize Vertex AI–based RAG systems (Vertex AI Search / Vertex AI RAG patterns), design reliable tool-using agents, and work comfortably with vector databases and graph databases. You’ll own end-to-end delivery: ingestion, retrieval, agent orchestration, evaluation, and deployment.

Responsibilities

• Collaborate with the team to design and develop high quality Web applications using Python, Flask, Django, and related technologies.
• Write clean and efficient code, and ensure code maintainability and reusability.
• Design and implement RAG pipelines on Google Cloud / Vertex AI (chunking, embeddings, indexing, retrieval, reranking, grounding).
• Build agentic workflows (tool use, planning, reflection/guardrails, structured outputs) using Python-first frameworks.
• Perform code reviews to ensure code quality and consistency.
• Conduct testing to ensure application quality and reliability.
• Create and maintain technical documentation for web applications.
• Participate in project planning, estimation, and prioritization.
• Stay up to date with the latest technologies for Python development.
• Define and run evaluation (retrieval metrics, answer quality, hallucination/grounding checks), and improve system quality iteratively.
• Ship to production: APIs, monitoring/observability, cost/performance optimization, CI/CD, and security best practices.

Qualifications

• Decent understanding of the software development/testing life cycle.
• Knowledge of relational databases (e.g. MySQL, PostgreSQL, etc).
• Experience with version control tools, such as Git.
• Experience building RAG solutions (hybrid search, reranking, chunking strategies, embeddings, prompt + schema design).
• Familiar with at least one agentic framework (e.g., LangGraph/LangChain, LlamaIndex, Semantic Kernel, AutoGen) and tool/function calling patterns.
• Solid knowledge of vector search concepts and at least one vector DB in production.
• Strong engineering practices: code reviews, testing, telemetry, secure‑by‑design, reliability mindset.

Preferred Skills

• Master’s Degree in Computer Science, Software Engineering, or related field.
• 1+ year professional experience in Python web application development with either Flask or Django.
• Experience in RESTful API development in Python.
• Understanding of Python web application frameworks such as Flask or Django.
• Experience with Cloud services, such as AWS.
• Experience with Vertex AI and GCP fundamentals (IAM, logging/monitoring, Cloud Run/GKE, storage).
• Knowledge graphs for RAG (entity linking, graph traversal + retrieval fusion).
• Streaming/messaging (Pub/Sub, Kafka), document pipelines (Document AI), and multilingual retrieval.
• Experience with evaluation tooling (RAGAS, TruLens, custom eval harnesses), prompt/version management.
• Frontend integration (basic React/Next.js) or platform enablement (internal developer tooling).