Role overview
As a hands-on lead engineer, you’ll design and build AI-powered services using LLMs, modern orchestration frameworks, and robust engineering practices. You’ll partner closely with data, product, and software teams to integrate these systems into real-world applications. You’ll also play a key role in growing our AI expertise & capability, developing frameworks/accelerators/best practices/etc. and mentoring our AI engineers.
Responsibilities
- Build scalable AI and GenAI systems using transformer-based models (e.g. GPT, Mistral, Claude) and RAG architectures
- Design and implement ML/AI pipelines including model training, evaluation,prompt chaining, embedding retrieval, and context management (MCP protocols)
- Engineer modular, well-tested Python code for AI agents, APIs, and microservices
- Apply ML Ops practices for reproducible training, deployment, and monitoring of models in production
- Use orchestration tools (LangChain, Semantic Kernel, n8n) to implement agent workflows and end-to-end AI experiences
- Collaborate with product and engineering teams to integrate AI into user-facing applications
- Partner with data engineering to build feature stores, vector search capabilities, and serve curated data
- Optimize AI systems for cost, latency, and scalability across Azure infrastructure (e.g., Azure ML, Azure AI Services)
- Lead on best practices around prompt evaluation, testing, model performance monitoring, and human-in-the-loop feedback
- Mentor and guide teammates (internally and at clients) on AI Engineering
- Champion responsible AI design, including bias mitigation and data privacy safeguards
Basic qualifications
- 7+ years of software or ML engineering experience, including 2+ years working on GenAI/LLM-based products
- Strong Python engineering skills (typing, testing, packaging, dependency management)
- Solid understanding of ML and NLP/LLM fundamentals—tokenization, attention, transformers, embeddings, supervised/unsupervised learning, etc.
- Hands-on experience building with LLMs, prompt chaining, and retrieval-augmented generation (RAG)
- Familiarity with Model Context Protocol (MCP) standards: schema design, context injection, context window management
- Experience with orchestration and agentic frameworks (LangChain, Semantic Kernel, GPT agents)
- Experience working in CI/CD environments with ML Ops tooling (e.g., MLflow, AzureML, Kubeflow)
- Deep understanding of API design, microservices, and distributed system architecture
- Experience deploying scalable workloads on cloud platforms (Azure preferred) using Docker/Kubernetes
- Proven experience mentoring engineers and leading technical workstreams
- Experience with vector databases (e.g., Pinecone, FAISS, Weaviate)
- Familiarity with serverless deployment patterns and infrastructure-as-code (e.g., Terraform, CDK)
- Exposure to human-in-the-loop feedback systems and ethical AI design
- Experience in AI governance, risk mitigation, and AI performance tuning
- Consulting or client-facing delivery experience in data/AI-driven environments
Benefits
- 25 days off per year plus closure between Christmas and New Year's.
- Flexible remote work from abroad options for up to 6 weeks per year.
- Learning & Development budget, including full access to Udemy courses.
- Classpass membership to support well-being.
- Latest tech & tools, including home office budget and professional software subscriptions.
- Equity share scheme to give long-term team members ownership in Riverflex.
- Be a Pioneer: Contribute to the development of Riverflex’s Software Engineering domain.
- Impactful Work: Work on high-profile projects with major clients like IKEA and deliver tangible results.
- Growth Opportunities: Gain exposure to advanced AI tools, machine learning, and enterprise-level software solutions in a dynamic environment.
- Supportive Culture: Work in a team that values innovation, creativity, and continuous learning.
Tags & focus areas
Used for matching and alerts on DevFound Fulltime Remote Ai Ai Engineer Machine Learning Generative Ai