Software Engineer

Responsibilities:

• Design and develop enterprise-grade Agentic AI applications with end-to-end ownership.
• Build scalable RAG systems and knowledge-driven AI experiences on enterprise data sources.
• Develop multi-agent workflows and intelligent automation solutions.
• Create backend services and APIs supporting AI-powered applications.
• Build AI-powered web applications and conversational experiences.
• Develop evaluation frameworks to improve AI quality, reliability, and safety.
• Optimise AI workflows for performance, scalability, and cost efficiency.
• Partner with product, data, and business teams to define AI-first solutions.
• Implement AI observability, governance, and responsible AI practices.
• Mentor junior engineers and contribute to engineering excellence and standards.
• Build cutting-edge machine learning models to feed the AI agents.
• Design scalable AI solution architectures aligned to business objectives and platform constraints.
• Build AI agents, autonomous workflows, and multi-agent orchestration systems
• Develop RAG pipelines ingesting and indexing enterprise knowledge sources.
• Implement prompt engineering strategies, model orchestration, and fine-tuning approaches.
• Build evaluation frameworks and AI quality assurance pipelines.
• Develop backend services and frontend applications supporting production AI solutions.
• Lead architecture discussions, technical design reviews, and code quality standards.
• Implement cloud-native deployment, monitoring, and CI/CD practices for AI workloads.
• Ensure AI governance, security, privacy, and compliance controls across all deliverables.
• Explore emerging agentic AI technologies and drive innovation within the practice.

Requirements:

• 5-6 years (3+ years hands-on AI / GenAI).
• Experience: 5-6 years of software engineering with strong full-stack expertise; 3+ years of hands-on AI/ML or generative AI.
• Education: Bachelor's or Master's degree in computer science, engineering, AI, or a related discipline.

Core Skills:

• Large Language Models (LLMs).
• Agentic AI Systems and Multi-Agent Architectures.
• Machine Learning (Classical + Applied).
• Retrieval-Augmented Generation (RAG).
• Prompt Engineering and Model Fine-Tuning.
• AI Evaluation and Observability.
• Semantic Search and Knowledge Retrieval.
• Python and Modern API Development.
• Full Stack Application Development.
• Cloud-Native Architecture (Azure / AWS / GCP).
• AI Governance and Responsible AI.
• MLOps and Production AI Deployment.

Behavioural Competencies:

• Strategic thinking with ability to translate business problems into AI solutions.
• Strong problem-solving and first-principles reasoning.
• Innovation mindset with bias toward experimentation and fast iteration.
• Customer focus and outcome orientation.
• Ownership and accountability across a full delivery lifecycle.
• Clear technical communication and cross-functional collaboration.
• Adaptability and learning agility in a fast-moving AI landscape.

Preferred Qualifications:

• Cloud or AI-related certifications (Azure AI, AWS ML Speciality, Google Professional ML Engineer).
• Experience building Agentic AI platforms at scale.
• Exposure to retail search, personalisation, recommendations, or commerce AI solutions.
• Contributions to open-source AI tooling or published technical writing.