AI Engineer

AI Engineer (LLM, GenAI & Agentic Systems) The ideal candidate is an AI engineer who can build and deploy real-world LLM and GenAI solutions. Experience delivering AI applications, agentic systems, RAG solutions, and AI-assisted engineering practices will be weighted more heavily than frontend framework expertise About the Role We are seeking a talented AI Software Engineer (LLM, GenAI & Agentic Systems) to join our growing AI Engineering team. This role focuses on designing, building, and deploying production-grade AI solutions that leverage Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), agentic workflows, and AI-assisted software engineering practices. You will work closely with solution architects, product teams, data scientists, and clients to transform AI concepts into scalable, secure, and business-ready applications. Beyond writing high-quality software, you will play a key role in shaping how AI technologies are applied responsibly and effectively across enterprise environments. Key Responsibilities AI Solution Development • Design, develop, test, and deploy AI-enabled applications using Large Language Models (LLMs), Generative AI technologies, and intelligent automation frameworks. • Integrate foundation models and AI services such as OpenAI, Azure OpenAI, Anthropic Claude, Amazon Bedrock, Google Gemini, Vertex AI, or equivalent platforms. • Develop AI-powered solutions including: • Enterprise chatbots and virtual assistants • Retrieval-Augmented Generation (RAG) applications • Intelligent document processing solutions • Knowledge management assistants • AI copilots and task automation agents • Multi-agent and agentic workflow systems • Translate business requirements into scalable AI solution architectures. • Partner with stakeholders to prototype, demonstrate, and productionize AI capabilities. Agentic Systems & Intelligent Automation • Design and implement agent-based architectures and workflow orchestration solutions. • Build systems that leverage tool/function calling, memory management, context engineering, and task planning. • Develop AI workflows that interact with enterprise applications, APIs, and knowledge repositories. • Evaluate and optimize agent performance for accuracy, reliability, latency, and operational efficiency. • Implement suitable safeguards and human-in-the-loop controls for critical business processes. Retrieval-Augmented Generation (RAG) & Knowledge Systems • Design and implement RAG architectures using embeddings, semantic search, and retrieval pipelines. • Develop ingestion, indexing, retrieval, and ranking workflows for enterprise knowledge repositories. • Optimize retrieval quality, grounding accuracy, and response relevance. • Work with vector search technologies, retrieval architectures, and semantic indexing approaches. • Evaluate retrieval effectiveness using structured testing methodologies and performance metrics. AI-Assisted Software Engineering • Utilize AI-assisted development tools such as GitHub Copilot, Cursor, Claude Code, Gemini Code Assist, Amazon Q Developer, JetBrains AI Assistant, Windsurf, or equivalent tools. • Apply prompt engineering and context management techniques to improve software development productivity. • Leverage AI tools for: • Code generation • Unit test generation • Documentation • Refactoring • Code reviews • Technical analysis • Critically review AI-generated outputs for correctness, maintainability, security, compliance, and performance. Software Engineering & Integration • Develop applications, services, APIs, and automation components primarily using Python and related technologies. • Design and implement RESTful APIs and integrations with third-party platforms and enterprise systems. • Build reusable AI services, SDKs, and internal accelerators. • Apply software engineering best practices including version control, testing, code reviews, and documentation. • Participate in architecture reviews and technical design discussions. AI Quality, Evaluation & Responsible AI • Define evaluation frameworks for AI applications and establish measurable quality criteria. • Use AI evaluation methodologies and tools to assess: • Accuracy • Grounding quality • Hallucination rates • Latency • User satisfaction • Implement guardrails, monitoring, and governance controls to support responsible AI adoption. • Ensure compliance with organisational security, privacy, data protection, and intellectual property policies. • Promote ethical and responsible use of AI technologies. Deployment & Operations • Package and deploy AI applications using containerisation technologies such as Docker. • Collaborate with DevOps teams to deploy AI services into cloud and enterprise environments. • Implement application logging, monitoring, observability, and AI performance telemetry. • Support production deployments and troubleshoot operational issues. Collaboration & Knowledge Sharing • Work closely with business stakeholders, product managers, architects, and data scientists. • Contribute to solution design workshops, proofs of concept, and client demonstrations. • Share best practices and mentor team members on AI engineering methodologies. • Create reusable assets, templates, reference implementations, and technical documentation. Required Qualifications • Bachelor's Degree, Master's Degree or equivalent practical experience in Computer Science, Information Technology, Engineering, Data Science, or related disciplines. • 3+ years of professional software development, AI engineering, automation engineering, or related experience. • Strong hands-on Python development experience. • Demonstrated experience building and deploying AI-enabled solutions using LLMs or Generative AI technologies. • Experience integrating foundation model APIs such as: • OpenAI • Azure OpenAI • Anthropic Claude • Google Gemini • Amazon Bedrock • Hugging Face • Equivalent platforms • Strong understanding of: • Prompt engineering • Context engineering • LLM fundamentals • Embeddings • Vector search • Retrieval-Augmented Generation (RAG) • Experience designing and implementing AI assistants, copilots, chatbots, agentic workflows, or intelligent automation solutions. • Familiarity with software engineering best practices including Git, code reviews, testing, and Agile delivery methodologies. • Strong problem-solving and analytical thinking skills. • Excellent communication and stakeholder engagement skills. Preferred Qualifications Candidates with one or more of the following will have an advantage: • Experience building multi-agent systems or autonomous workflows. • Experience with AI orchestration frameworks such as: • LangChain • LlamaIndex • Semantic Kernel • CrewAI • AutoGen • OpenAI Agents SDK • PydanticAI • Equivalent frameworks • Experience with vector databases or retrieval platforms such as: • Pinecone • Weaviate • FAISS • pgvector • Chroma • Milvus • Experience with AI evaluation frameworks such as Promptfoo, RAGAS, DeepEval, or equivalent. • Experience deploying solutions using Docker and container technologies. • Exposure to AWS, Azure, or GCP AI services. • Experience with frontend technologies such as React, Angular, or modern web frameworks. • Familiarity with CI/CD pipelines and DevOps practices.