Senior AI Engineer
Further
Salary undisclosed
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Description
WE’RE HIRING! If you love data and are looking for unlimited growth opportunities, we want to talk with you about joining Further.
Further is a data, cloud, and AI company whose focus is helping companies turn raw data into the right decisions. We have an award winning culture of extraordinary people. Our purpose is to enable people to thrive so that businesses can thrive. We believe that the work you do should matter - it should be meaningful to you professionally and personally, and it should have a positive impact on both you and our clients. If this sounds exciting to you, let’s chat!
SENIOR AI ENGINEER
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.
What experience should you have:
- 12+ years of years experience developing and deploying Data Science and ML solutions
- 3+ years of experience working directly with GenAI/RAG/LLM architecture
- 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.
- Nice to have: Experience in Typescript development for prototyping and integrations
- Nice to have: Proficiency with git workflows and understanding of standard application development processes
Preferred Qualifications
- 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.
- Any Google Cloud Professional Certification
What you’ll be doing in this role:
- Lead the implementation of rigorous evaluation frameworks to monitor model performance, drift, and cost in real-time.
- 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.
- Establish comprehensive model observability and guardrail systems to monitor real-time performance, detect distribution drift, and implement automated safety filters that mitigate hallucinations, bias, and toxic outputs in production environments.
- 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.
- Define the end-to-end architecture for AI products on cloud platforms (preferably Google Cloud Platform), ensuring high availability, security, and cost-effectiveness.
What you’ll need to accomplish in your first year:
- 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.
Our total rewards program is designed for your protection, peace of mind, and overall well-being. In addition to our outstanding basics, we offer a net-zero cost medical option, company contributions to your HSA, fertility support, fully-paid parental leave, a monthly stipend for your lifestyle spending account, and much more.
Apply today or check out all our opportunities!
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About this role
Further, a data and AI company in Cleveland, is looking for a Senior AI Engineer to lead the development of production-grade AI products on Google Cloud. This role sits at the intersection of data science and software engineering—you'll take experimental prototypes and transform them into reliable, enterprise-ready systems that clients can depend on. The work involves architecting backend services, designing RAG systems with vector databases, implementing LLMOps infrastructure, and building the guardrails and observability needed to keep AI models performing safely and cost-effectively at scale.
You'll need at least 12 years of experience developing and deploying data science and ML solutions, with at least 3 years working specifically with GenAI, RAG, and LLM architectures. Deep expertise in Python, modern API design, LLM frameworks like LangChain or LlamaIndex, and vector databases is essential. You should also have strong hands-on knowledge of LLMOps principles and Google Cloud infrastructure. Beyond technical skills, the role expects you to lead and mentor engineers, conduct code reviews, collaborate across teams, and establish architectural standards that will accelerate delivery across multiple client projects.
In your first year, you'll develop reusable internal libraries and patterns, mentor the team on building deterministic software around probabilistic models, and help define end-to-end product architecture. This is an in-office position in Cleveland. Further offers comprehensive benefits including net-zero cost medical options, HSA contributions, fertility support, fully-paid parental leave, and a lifestyle spending account.
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