AI Software Engineer Talent Network | London | Open Application
FemTechConf Partner Network
Build the Next Generation of AI-Powered Products in London
Are you an AI Software Engineer, Machine Learning Engineer, Applied AI Engineer, Software Engineer, Backend Engineer, Data Engineer, LLM Engineer, AI Product Engineer, or Full-Stack Engineer with experience or interest in artificial intelligence?
FemTechConf is building a network of exceptional AI and software engineering talent interested in connecting with our clients and partner organisations across the London and UK technology ecosystem.
By applying to this opportunity, your profile may be shared with selected FemTechConf clients and partner organisations that are actively looking for engineers across areas such as:
- AI Software Engineering
- Machine Learning Engineering
- AI Product Development
- Automation
- AI Infrastructure
Our clients and partners include startups, scale‑ups, global technology companies, consulting organisations, enterprise technology teams, AI‑focused companies, research‑driven teams, and organisations investing in software, data, cloud, automation, and artificial intelligence.
This is an open application opportunity designed to connect strong AI and software engineering talent with current and future roles across our client and partner network.
What Our Clients Are Looking For
While requirements vary depending on the organisation, our clients are typically interested in AI software engineers with experience across one or more of the following areas:
AI Software Engineering
- Building AI‑powered products, platforms, tools, or internal systems
- Integrating AI models into production software
- Developing intelligent features for web, mobile, SaaS, enterprise, or customer‑facing products
- Working with APIs, backend systems, data pipelines, and AI services
- Building scalable, reliable, and user‑focused AI applications
- Translating AI concepts into practical product features
Machine Learning and Applied AI
- Machine learning model development, training, fine‑tuning, or evaluation
- Applied AI use cases across automation, prediction, classification, recommendation, search, or personalisation
- Python, PyTorch, TensorFlow, scikit‑learn, Hugging Face, or similar tools
- Model evaluation, experimentation, and performance improvement
- ML pipelines, feature engineering, and model deployment
- Responsible and reliable AI implementation
LLMs, Generative AI and AI Agents
- Large language models and generative AI applications
- Prompt engineering and AI workflow design
- Retrieval‑augmented generation, vector databases, embeddings, and semantic search
- AI agents, copilots, chatbots, automation tools, and intelligent assistants
- Evaluation, guardrails, monitoring, and reliability for LLM‑based systems
Backend, Data and Cloud Engineering
- REST APIs, GraphQL, microservices, and distributed systems
- Databases such as PostgreSQL, MongoDB, Redis, Snowflake, BigQuery, or similar
- Data pipelines and analytics infrastructure
- AWS, Azure, Google Cloud, or similar cloud platforms
- Docker, Kubernetes, CI/CD, observability, and deployment workflows
Product, Collaboration and Engineering Quality
- Working closely with product managers, designers, data scientists, researchers, and business teams
- Writing clean, scalable, maintainable, and well‑tested code
- Turning ambiguous problems into usable technical solutions
- Building prototypes, MVPs, and production‑ready features
- Code reviews, technical documentation, and engineering best practices
- User‑focused product thinking and strong communication skills
Typical Responsibilities
Depending on the client organisation and opportunity, responsibilities may include:
- Designing, building, testing, and maintaining AI‑powered software products, platforms, and internal tools
- Integrating machine learning models, LLMs, APIs, or AI services into production systems
- Developing backend, full‑stack, data, cloud, or AI infrastructure solutions
- Building AI agents, copilots, recommendation systems, automation tools, or intelligent product features
- Collaborating with product managers, designers, data scientists, researchers, and other engineers
- Supporting model evaluation, experimentation, deployment, and monitoring
- Improving the performance, reliability, scalability, and safety of AI‑enabled systems
- Building data pipelines, vector search systems, retrieval systems, or AI workflows
- Participating in code reviews, technical discussions, and agile development processes
- Contributing to architecture decisions for AI, data, backend, cloud, and platform systems
- Helping teams turn AI ideas into useful, reliable, and impactful products
Ideal Candidate
We welcome applications from engineers with experience across AI software engineering, machine learning engineering, applied AI, backend engineering, data engineering, full‑stack engineering, cloud engineering, or product engineering.
Successful candidates may have:
- Professional experience as an AI Software Engineer, Machine Learning Engineer, Applied AI Engineer, Software Engineer, Backend Engineer, Data Engineer, Full‑Stack Engineer, LLM Engineer, or similar role
- Experience building production software, AI applications, data systems, platforms, or APIs
- Understanding of AI, machine learning, LLMs, data pipelines, automation, or intelligent systems
- Experience with tools such as PyTorch, TensorFlow, Hugging Face, OpenAI, LangChain, vector databases, cloud platforms, or similar technologies
- Ability to write clean, scalable, and maintainable code
- Experience working in agile product or engineering teams
- Strong communication skills and the ability to work with technical and non‑technical stakeholders
- Interest in London‑based, UK‑based, hybrid, or remote AI and software engineering opportunities
- Passion for artificial intelligence, product development, innovation, and continuous learning
You do not need to have every skill listed. We encourage applications from candidates with different technical backgrounds, career paths, and levels of experience.
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