Senior AI Engineer

The candidate will have responsibilities across the following functions:

AI Solution Design and Architecture:

• Design and architect scalable, production-ready AI/ML solutions for enterprise use cases.
• Translate complex business problems into practical AI-driven solutions.
• Define technical architecture, technology stack, APIs, data flows, and integration patterns.
• Evaluate and select appropriate LLMs, AI models, frameworks, and cloud technologies.

Generative AI and LLM Engineering:

• Lead development of GenAI applications using LLMs, RAG, AI Agents, embeddings, vector databases, and prompt engineering.
• Build and optimise AI agents and multi-agent workflows for enterprise use cases.
• Work with models from providers such as OpenAI, Anthropic, Google, and open-source ecosystems.
• Implement techniques for improving model accuracy, reliability, latency, and cost efficiency.

End-to-End AI Development:

• Own AI projects from POC and prototyping through production deployment.
• Develop clean, scalable, maintainable, and production-grade code.
• Build AI pipelines, model-serving layers, APIs, integrations, and supporting backend components.
• Establish appropriate testing and validation frameworks for AI solutions.

Technical Leadership:

• Provide technical direction and mentorship to AI Engineers and Developers.
• Conduct code reviews, architecture reviews, and technical design discussions.
• Establish engineering best practices, coding standards, documentation, and development processes.
• Break down complex requirements into technical tasks and guide the team toward successful delivery.

Enterprise Client Solutions:

• Work closely with clients, business stakeholders, and internal teams to understand business challenges and define AI solutions.
• Participate in client discussions, solution workshops, technical presentations, and solution demonstrations.
• Explain complex AI concepts and technical approaches in a clear, business-oriented manner.
• Identify opportunities where AI can deliver measurable business value.

AI/ML Model Evaluation and Optimisation:

• Design evaluation frameworks to measure model quality, accuracy, relevance, hallucination, latency, and cost.
• Continuously benchmark models and identify opportunities for improvement.
• Optimise prompts, RAG pipelines, model selection, inference, and AI workflows.
• Ensure AI solutions are reliable and suitable for production environments.

Cloud and Production Engineering:

• Design and deploy AI workloads on cloud platforms such as AWS, Azure, or GCP.
• Work with containerization, APIs, databases, CI/CD, monitoring, and scalable infrastructure.
• Ensure solutions meet enterprise requirements around performance, security, scalability, and availability.
• Collaborate with DevOps and cloud teams for production deployment and operations.

AI Governance, Security and Responsible AI:

• Ensure AI solutions follow appropriate security, privacy, governance, and compliance practices.
• Identify risks related to data privacy, hallucination, bias, prompt injection, and unauthorised access.
• Implement appropriate safeguards and monitoring mechanisms for enterprise AI applications.

Research and Innovation:

• Stay updated with the latest developments in Generative AI, LLMs, AI Agents, ML frameworks, and emerging AI technologies.
• Evaluate new tools, models, and frameworks for potential business applications.
• Build rapid POCs to validate emerging technologies and ideas.
• Contribute to EMB Global's internal AI accelerators, reusable frameworks, and solution assets.