Senior AI Engineer

Further•Atlanta, GA

4d•Remote

About The Position

We are hiring a Senior AI Engineer to lead the development of our cloud-based (Google Cloud), commercial AI products. You will bridge the gap between experimental data science prototypes and production-grade software. You will architect the robust systems and LLMOps workflows necessary to transform AI models into reliable, enterprise-ready applications. By designing stable backend architectures and seamless integration layers, you will ensure our AI solutions are not just functional, but ethical, efficient, and high-value products that meet the rigorous demands of our commercial clients.

Requirements

6+ years of software engineering experience with at least 3 years dedicated to AI/ML application development.
Expert proficiency in Python AI application development and modern API architecture (REST, GraphQL, gRPC) using enterprise standards like static type checking and data validation.
Deep experience building production applications with LLM frameworks such as LangChain, LangGraph or LlamaIndex.
Hands-on expertise with vector databases (Pinecone, Weaviate, PostgreSQL) and search algorithms.
Strong understanding of LLMOps principles, including model registry, versioning, and serving infrastructure specifically in Google Cloud.
Experience in Typescript development for prototyping and integrations.
Proficiency with git workflows and understanding of standard application development processes.

Nice To Haves

Knowledge of advanced prompt engineering and fine-tuning techniques (LoRA, PEFT).
Experience optimizing inference costs and latency for large-scale deployments.
Previous experience in a client-facing consulting role, managing diverse stakeholders and navigating complex organizational structures.

Responsibilities

Define the end-to-end architecture for AI products on Google Cloud Platform (GCP), ensuring high availability, security, and cost-effectiveness.
Architect and develop high-performance backend services and APIs using Python (FastAPI) to serve large language models at scale.
Design advanced Retrieval-Augmented Generation (RAG) systems, selecting and managing vector databases and optimizing embedding strategies for accuracy and speed.
Build robust integration layers that connect AI agents securely to external enterprise systems, CRMs, and legacy databases.
Conduct code reviews, provide technical guidance, and foster a culture of continuous learning and innovation within the engineering team.
Collaborate with infrastructure teams to define deployment strategies, ensuring solutions scale dynamically under load.
Lead the implementation of rigorous evaluation frameworks to monitor model performance, drift, and cost in real-time.
Develop reusable internal libraries and architectural patterns and standards to accelerate the delivery of AI solutions across multiple client engagements.
Mentor engineers on best practices for building deterministic software around probabilistic AI models.