Pinitech LTD
Generative AI Engineer
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Role Overview
We are seeking an experienced AI Engineer to lead the design, development and deployment of machine learning and AI-driven solutions across our product portfolio.
The successful candidate will contribute throughout the full development lifecycle, from initial research and prototyping through to production deployment, monitoring and continuous improvement. Working closely with product, engineering and data teams, the AI Engineer will translate business and product requirements into secure, scalable and commercially effective AI systems.
Key Responsibilities
- Design, develop, train, fine-tune and evaluate machine learning and artificial intelligence models, including large language models, natural language processing, computer vision and recommendation systems.
- Develop and maintain robust data pipelines to support model training, evaluation and real-time or batch inference.
- Integrate AI and LLM capabilities into production applications using third-party APIs, including OpenAI and Anthropic, as well as open-source models.
- Design and implement Retrieval-Augmented Generation (RAG) architectures, embedding-based search, prompt-engineering solutions and agentic workflows.
- Establish and maintain MLOps processes, including model versioning, automated testing, CI/CD, deployment, monitoring and observability.
- Work collaboratively with product managers, software engineers and other stakeholders to define technical requirements and delivery plans for AI-enabled features.
- Evaluate model performance and optimise solutions for accuracy, reliability, scalability, latency and cost efficiency.
- Monitor production systems, investigate performance issues and implement appropriate improvements.
- Assess emerging AI technologies, frameworks and research to determine their suitability for business and product use cases.
- Apply responsible AI principles, including data privacy, security, transparency, bias mitigation and compliance with UK GDPR and other applicable requirements.
- Produce and maintain clear technical documentation relating to system architecture, model development, deployment and operational procedures.
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Essential Experience and Qualifications
- A minimum of two years’ relevant professional experience in AI engineering, machine learning engineering, applied data science or a comparable role.
- Advanced proficiency in Python and experience using machine learning frameworks such as PyTorch, TensorFlow or equivalent.
- Practical experience developing solutions involving LLMs, embeddings and vector databases such as Pinecone, Weaviate, FAISS or similar technologies.
- Experience designing, implementing and evaluating RAG-based systems.
- Experience integrating AI services and models into production applications through REST, GraphQL or comparable APIs.
- A sound understanding of backend development and production software architecture.
- Experience working with at least one major cloud platform, such as AWS, Google Cloud Platform or Microsoft Azure.
- Familiarity with containerisation and orchestration technologies, including Docker and Kubernetes.
- Strong knowledge of software engineering practices, including version control, code review, automated testing and CI/CD.
- An understanding of model evaluation, monitoring and performance optimisation.
- A degree in Computer Science, Artificial Intelligence, Data Science, Engineering or a related discipline, or equivalent professional experience.
- Strong analytical, problem-solving and decision-making capabilities.
- Effective written and verbal communication skills, with the ability to work collaboratively across technical and non-technical teams.
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Desirable Experience
- Experience using agentic AI frameworks such as LangChain, LlamaIndex or AutoGen.
- Experience fine-tuning, evaluating and deploying open-source large language models.
- Knowledge of AI safety measures, model guardrails, hallucination mitigation and output-quality evaluation.
- Experience building scalable vector-search or semantic-search systems.
- Experience working with cloud-based machine learning services and infrastructure-as-code tools.
- Published research, relevant open-source contributions or participation in recognised machine learning competitions.
- Experience working within a technology-led, product-focused or high-growth organisation.
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