Lead AI Engineer

Jobtailor

Crawley

On-site

GBP 110,000 - 170,000

Full time

14 days+

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Job summary

Jobtailor is seeking a Technical Leader to design and scale agentic AI systems, drive enterprise-grade LLM deployment, and define engineering excellence in collaboration with architecture and product teams. The role focuses on mentoring engineers, delivering MVPs to production, and shaping the AI strategy across the business.

You'll work with GCP Vertex AI, LangGraph, LangChain, and MLOps pipelines to ensure scalable, secure, and cost-effective solutions.

Qualifications

Experience building complex LLM-powered systems and multi-agent workflows.
Deep ML/NLP knowledge and enterprise model deployment.
Hands-on design of ELT/ETL data pipelines on cloud platforms.
Proven leadership in delivering technical projects and mentoring juniors.

Responsibilities

Lead AI engineering deliverables and design agentic workflows.
Own LLMOps/LLMOps CI/CD pipelines and deployment patterns.
Mentor engineers and drive MVPs to production at scale.
Collaborate with product and enterprise architects on standards and safety.

Skills

AI Orchestration & Development

MLOps/LLMOps Pipelines

Data Engineering & Cloud Computing

Team Leadership & Mentorship

Artificial Intelligence & Machine Lear

Autonomy

Influence

Complexity

Business Skills

Python

SQL

Education

Google Cloud Certified Cloud Engineer

Google Cloud Certified Professional Data Engineer

Google Cloud Certified Cloud AI Engineer

Bachelor’s or Master’s in CS/SE/AI or equivalent

Tools

Google Cloud Platform

Vertex AI

BigQuery

Cloud SQL

Google Cloud Storage

Docker

Kubernetes

LangGraph

LangChain

AutoGen

Job description

**Technical Leadership & Engineering**
Design Agentic Systems: Design and scale robust, secure, and production-ready multi-agent workflows, orchestrations, and advanced RAG architectures, in collaboration with Enterprise Architecture principles. Drive delivery by designing and building agentic solutions, spanning from piloting to full implementation.
Define Engineering Excellence: Establish strict coding standards, code review processes, testing frameworks, and evaluation metrics for generative AI applications. Support and strictly enforce the standards set by the Head of Engineering and Technical Architect.
Cloud & Platform Integration: Partner closely with our GCP and Data Engineering teams to build seamless LLMOps/MLOps CI/CD pipelines, ensuring scalable and cost-effective model deployment via Vertex AI and containerized environments. Take ownership of building and maintaining robust LLMOps pipelines.
AI Safety & Guardrails: Implement robust evaluation frameworks, latency monitoring, and automated guardrails to ensure enterprise-grade safety, security, and compliance.
Internal AI Enablement & Prompt Lifecycle: Manage the engineering workflows, CI/CD pipelines, version control, and evaluation frameworks for internal developer-facing AI assets, including prompt libraries and automated testing agents.
**Team Mentorship & Delivery**
Grow the team: Act as a technical mentor to a team of intermediate and junior AI Engineers, fostering a culture of continuous learning, clean code, and agility.
Pragmatic Delivery: Collaborate with the AI Product Owner and Business Analysts to translate abstract business use cases into structured, achievable technical sprints.
Drive MVP to Production: Shift the team’s focus from sandboxed proof-of-concepts (PoCs) to reliable, resilient applications deployed to production for global users, leading AI engineering for AI team solutions and actively supporting junior engineers through this transition.
**Evolve AI Maturity**
Support the company’s evolving AI strategy, providing an expert voice on Use Case identification, platform identification and tool selection.
Advise the AI portfolio Lead in scaling impact and AI capability across the company, beyond the Group AI Team.
Stay up to date on market trends, new opportunities, and the changing landscape of AI technologies.

Requirements

**Experience**
**AI Orchestration & Development:** Expert-level experience building complex LLM-powered systems and multi-agent workflows using frameworks like LangGraph, LangChain, AutoGen, or ADK.
**Artificial Intelligence & Machine Learning:** Deep practical understanding of machine learning algorithms, natural language processing (NLP) techniques, and the optimization of large language models for enterprise deployment.
**Data Engineering & Cloud Computing:** Strong proficiency in designing optimized ELT/ETL pipelines and managing data lake/data warehouse architectures. Hands‑on experience with Google Cloud Platform (GCP) services including Vertex AI, BigQuery, Cloud SQL, and Google Cloud Storage (GCS).
**MLOps/LLMOps Pipelines:** Proven track record of architecting pipelines for model deployment, performance tracking, hyperparameter tuning, and containerized workflows using Docker and Kubernetes (GKE/Vertex AI Pipelines).
**Team Leadership & Mentorship:** Extensive experience leading technical delivery, defining engineering milestones, running code reviews, and successfully mentoring junior or intermediate engineering talent.
**Knowledge & Skills**
**Autonomy:** Works with high autonomy under broad strategic guidance. Accountable for defining and meeting technical, architectural, and delivery goals, establishing milestones, and assigning tasks.
**Influence:** Exerts major technical influence across the organization, partners, and peers. Partners with cross‑functional business and product leaders, making key architectural decisions that affect budgets, timelines, and scalability.
**Complexity:** Manages diverse, highly complex, and unpredictable technical challenges. Uses fundamental engineering principles to connect cutting‑edge AI research with practical enterprise software.
**Business Skills:** Advises executives on AI trends, risks, and tools. Bridges the gap between technical developers and business teams, showcasing strong leadership, creativity, and ethical problem‑solving.
**Technical Expertise & Tools**:
Python: Advanced, production‑grade proficiency (Pandas, NumPy, FastAPI/Flask) with an absolute focus on writing clean, modular, and highly testable code.
SQL: Expert querying, database design, partitioning, and optimization strategies for large‑scale BigQuery environments.
**Relevant Qualifications**
Desirable:
Google Cloud Certified Cloud Engineer
Google Cloud Certified Professional Data Engineer / Cloud AI Engineer
Degree Qualification or Equivalent: A Bachelor’s or Master’s degree in Computer Science, Software Engineering, Artificial Intelligence, or a highly quantitative field is desirable; however, a proven track record of architecting and shipping production‑grade commercial AI systems is highly valued and may substitute for specific academic credentials. IT HNC/HND courses will also be accepted.

Core Competencies

Demonstrates expertise in AI Orchestration and Development, with a strong focus on building complex LLM-powered systems and multi‑agent workflows. Proficient in Cloud Computing and Data Engineering, particularly with Google Cloud Platform services, while also showcasing leadership in team mentorship and technical delivery.

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