Senior AI Engineer (Full-Stack) (Remote)

Codvo.ai

13h•Remote

About The Position

We are looking for a Senior AI Engineer with strong full-stack capabilities to join our engineering team. You will work across multiple client-facing projects that sit at the intersection of AI, data, and web—starting with a live production AI agent platform. You should be comfortable owning features end-to-end, from LLM pipeline design through to a polished frontend experience.

Requirements

AI Agent Engineering: LangGraph or equivalent graph-based agent frameworks.
Multi-step reasoning pipelines.
Tool usage and orchestration.
State management and conversational workflows.
RAG & Vector Search: End-to-end RAG pipeline design and implementation.
Experience with vector databases such as: Pinecone, Qdrant, pgvector, Weaviate.
Chunking strategies and retrieval optimization.
Retrieval evaluation methodologies.
LLM Integration: OpenAI, Gemini, and Anthropic SDKs.
Prompt engineering and prompt optimization.
Structured JSON outputs.
Context window management.
Multi-provider LLM integrations.
Python Backend Development: Python 3.12, FastAPI, Async Python, Pydantic, SQLite, PostgreSQL, Redis, Pytest.
Full-Stack Development: React, Next.js, TypeScript, Modern frontend architecture, API integration and state management.
ML Engineering Fundamentals: Evaluation pipelines, Golden datasets and test suites, Regression tracking, Model performance monitoring.
Ability to independently own a feature from requirements gathering to production deployment.
Strong full-stack engineering capabilities with no hand-holding required between backend and frontend development.
Strong engineering judgment and the ability to push back when shortcuts introduce hallucination risks, reliability issues, or technical debt.
Comfortable working in ambiguous environments with evolving client requirements.
Experience delivering software in real-world production environments.
Excellent communication skills with the ability to explain AI system behavior and limitations to non-technical stakeholders.
Strong documentation and testing discipline.

Nice To Haves

GIS & Mapping: ArcGIS REST APIs, GeoJSON, MapLibre GL JS, Spatial queries (Strong advantage for initial project assignments.)
Data Visualization: Recharts, D3.js, Equivalent charting libraries.
Cloud & DevOps: Docker, Azure, AWS, CI/CD pipelines, OIDC Authentication.
Product Thinking: Ability to understand and interpret Figma designs.
Evaluate trade-offs between engineering effort and business value.
Deliver solutions aligned with business objectives.
Experience in any of the following industries is a significant advantage, though not required: Energy, Oil & Gas, Infrastructure, Enterprise GIS.

Responsibilities

Design and build AI agent pipelines, including multi-node LangGraph graphs.
Implement intent routing, multi-turn conversational context, session state management, and tool integrations.
Develop multi-step reasoning pipelines and graph-based agent workflows.
Build and maintain Retrieval-Augmented Generation (RAG) systems.
Design vector search architectures, embedding pipelines, retrieval grounding, and chunking strategies.
Implement hallucination mitigation techniques and retrieval evaluation frameworks.
Integrate and optimize Large Language Models (LLMs) including OpenAI, Gemini, and Anthropic.
Develop structured output workflows using JSON schemas.
Create effective prompt engineering strategies, few-shot examples, and context window management solutions.
Build provider-neutral client architectures to support multiple LLM vendors.
Design, develop, and deploy end-to-end product features.
Build scalable FastAPI backends and React/Next.js frontends.
Implement Server-Sent Events (SSE) streaming and REST API contracts.
Deliver production-ready UI features independently without requiring dedicated frontend support.
Own LLM observability including: Token usage logging, Cost tracking, Fallback detection, Performance monitoring.
Build evaluation pipelines and golden test suites to ensure AI quality and consistency.
Collaborate directly with clients and stakeholders to understand business requirements.
Translate requirements into scalable, maintainable software solutions.
Keep technical documentation, specifications, and test coverage aligned with product changes.