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.