[Hiring] AI Development Engineer @Proximity Works

Role Description

We are looking for an AI Development Engineer who is passionate about solving complex engineering problems and using AI to improve the speed, quality, and cost efficiency of software development. The role involves rethinking the traditional approach to software engineering — moving from coding and implementation-heavy workflows to solution orchestration, structured thinking, detailed specifications, system design, and validation. You will work on building intelligent, agentic applications that combine AI capabilities with traditional engineering systems. The focus is on using AI effectively while ensuring that the solutions we build remain reliable, secure, scalable, maintainable, and production-ready.

Key Responsibilities

• Requirement Clarity and Engineering Design
  • Develop a deep understanding of technical requirements and system-level design to minimise rework and reduce the risk of AI-generated errors.
  • Translate ambiguous product and business ideas into clear, structured technical specifications.
  • Create specifications that serve as a reliable source of truth for both engineering teams and AI systems.
• Engineering Excellence
  • Establish practices to minimise functional defects and operational issues caused by AI-generated code.
  • Identify and eliminate code bloat, redundant logic, dead code, and unnecessary complexity.
  • Ensure AI-generated code remains understandable, maintainable, and easy for engineers to navigate.
  • Build the right documentation and engineering artefacts so that AI-generated code does not become a black box.
  • Incorporate security best practices into prompts, context, and code-generation workflows.
• Agentic Applications
  • Build, maintain, and scale production systems that leverage Generative AI to transform user experiences.
  • Design and monitor agentic workflows where AI components interact with traditional microservices to solve complex business problems.
  • Explore and implement ways to move from manual workflows to intelligent, agent-driven applications.
• Operational Excellence
  • Focus on rigorous validation as AI accelerates development and output.
  • Ensure automated outputs meet high standards of security, performance, reliability, and intended functionality.
  • Build deep observability into services and systems to identify and address production issues.
  • Explore self-healing production workflows where AI can interpret logs, identify issues, and suggest real-time fixes.
• Cost Efficiency
  • Optimise inference costs through effective prompt engineering, model selection, tool usage, and workflow design.
  • Balance model performance, token consumption, and overall business ROI.
  • Identify and reduce technical debt caused by AI-generated code and redundant implementations before they affect system performance or cloud costs.
• Organisational Leadership and Collaboration
  • Work closely with Product, Production Engineering, Security, and other stakeholders to align AI capabilities with business goals and responsible AI practices.
  • Encourage a culture where engineers can experiment with new AI workflows, learn from failures, and continuously improve.
  • Mentor junior engineers and help drive the shift from traditional coding practices towards AI-assisted engineering and system orchestration.

Qualifications

• Strong foundations in Machine Learning and AI, including linear algebra, probability and statistics, supervised and unsupervised learning algorithms, and standard MLP/DNN architectures.
• Understanding of bias-variance trade-offs, model fitting and selection, hyperparameter tuning, and model performance evaluation.
• 5+ years of hands-on experience and working knowledge of Generative AI foundation models and LLM building blocks, including:
  • Transformers for vision and text
  • Tokenisation and attention mechanisms
  • Prompting techniques
  • Supervised Fine-Tuning (SFT) and Reinforcement Learning from Human Feedback (RLHF) concepts
  • Retrieval-Augmented Generation (RAG)
• Hands-on experience with prominent ML frameworks and libraries such as PyTorch, TensorFlow, and Hugging Face.
• Experience building or working on AI applications across one or more domains, including text, vision, speech, audio, and video.
• Ability to understand complex engineering requirements and translate them into structured specifications, implementation plans, and validation workflows.
• Strong problem-solving skills and an interest in exploring how AI can improve engineering workflows and application development.

Benefits

• Best-in-class salary: We hire strong talent and compensate accordingly.
• Proximity Talks: Meet and learn from designers, engineers, product leaders, and AI practitioners.
• Continuous learning: Work with a world-class team and stay close to the latest in AI, engineering, and product development.
• High-impact work: Build AI-first systems and products used at scale by global clients.