Job Specifications
Agentic AI Solutions Engineer
6-month contract – Inside IR35 – day rate negotiable
London based – hybrid working – 2/3 days on site
Ideally you would have worked in the aviation/airline sector previously
Job Description Summary
Join our global Centre of Excellence as a subject matter expert (SME) for Agentic AI Services, collaborating with teams worldwide to drive innovation and excellence. As part of this central function, you will help shape, implement, and scale cutting-edge Agentic AI solutions across the organization and our clients.
This hands-on role requires deep expertise in artificial intelligence, machine learning, and software development to design, build, and deploy advanced Agentic AI-driven solutions tailored to clients' unique needs. The Agentic AI Solutions Engineer will focus on implementing intelligent agents and leveraging AI technologies to streamline and enhance business operations, delivering measurable value and aligning closely with strategic goals.
The Agentic AI Solutions Engineer will be instrumental in advancing the organization’s research and development capabilities. This includes investigating the latest developments in agentic AI and machine learning, experimenting with novel approaches, and transforming research insights into working prototypes. The role also involves maintaining and evolving our product portfolio of Agentic AI Agents, as well as designing and developing compelling demos to showcase innovations. Close collaboration with research and product teams is essential to ensure that R&D efforts translate into impactful, real-world solutions that enhance the company’s competitive advantage.
The Agentic AI Solutions Engineer will actively participate in identifying high-value use cases, assessing solution feasibility, prototyping innovative ideas, and delivering successful pilot projects. The role demands strong technical knowledge, proficiency in software engineering, and capability in data engineering and cloud infrastructure. Additionally, the Agentic AI Engineer is expected to ensure secure, reliable, and scalable integrations with existing enterprise platforms, systems, and data sources.
Key Responsibilities:
Develop, fine-tune, and deploy AI models, including large language models (LLMs) such as GPT-4 or open-source equivalents.
Design and implement effective prompt engineering strategies and optimizations to enhance AI accuracy, consistency, and reliability.
Engage with internal stakeholders and clients to understand business needs, translating them into actionable AI solutions.
Rapidly prototype, test, and iterate AI applications using advanced Python programming and relevant frameworks.
Integrate AI solutions securely with existing enterprise systems (CRM, ERP, HRIS, finance platforms, collaboration software) via API development and integration.
Build, maintain, and optimize end-to-end data pipelines to ensure accurate and timely data delivery for AI models.
Manage structured and unstructured datasets, leveraging vector databases and semantic search to enhance knowledge management capabilities.
Deploy, manage, and scale AI solutions within cloud computing environments (Azure, AWS, GCP), ensuring high availability, performance, and cost efficiency.
Implement DevOps and MLOps practices, including automated deployment, testing, monitoring, and version control, to efficiently manage the AI model lifecycle.
Ensure AI solutions adhere to industry standards and compliance regulations (GDPR, HIPAA), emphasizing security and privacy best practices.
Identify and mitigate risks associated with AI deployments, proactively addressing ethical considerations, biases, and unintended consequences.
Collaborate closely with business and functional teams to streamline processes through intelligent automation and deliver measurable business outcomes.
Provide clear documentation of technical designs, project plans, and operational procedures.
Contribute to the continuous improvement of AI best practices, methodologies, and internal frameworks.
Stay abreast of the latest AI and machine learning developments, continuously evaluating emerging technologies and methodologies.
Hands-on experience with Microsoft Copilot Studio, Azure AI Foundry, and Semantic Kernel is highly desirable and considered a strong advantage for this role.
Knowledge and Attributes:
Deep understanding of artificial intelligence, natural language processing (NLP), and machine learning principles.
Expertise in selecting, fine-tuning, and deploying large and small language models (LLMs/SLMs), such as OpenAI’s GPT series and open-source alternatives.
Proven experience with prompt engineering, prompt optimization, and AI model reliability and accuracy improvements.
Advanced proficiency in Python programming, essential for rapid prototyping, integration, and model implementation. Python is the preferred language for AI; strong proficiency in Python is essential due to the extensive use of frameworks, libra