AI/ML Developer

About the Role

Job Title: Developer – AI Engineering / LLM & Agentic Systems
Client Sector / Domain Preference: Artificial Intelligence, Enterprise AI Solutions, LLMs and Agentic Systems
Working Model: 5 days a week, Work from Office
Role Type: Permanent

Role Overview

We are seeking a highly experienced, hands-on Developer – AI Engineering with 6–8 years of experience in software engineering and AI development, including recent practical experience building and deploying production-grade Large Language Model (LLM) and agentic AI systems.

The primary purpose of this role is to lead the engineering build-and-deploy effort, supervise the development team and ensure the high-quality implementation of AI solutions in alignment with architecture, design patterns and technical standards defined by external technology and consulting partners, including Microsoft and McKinsey.

The successful candidate will act as the technical counterpart to partner architects, understanding and critically evaluating proposed designs, raising technical clarifications, identifying implementation risks and ensuring that engineering delivery meets agreed quality, security and performance standards.

This is a hands-on technical leadership position requiring demonstrated AI implementation experience alongside strong software engineering discipline. The candidate will build core components and reusable reference implementations, govern the codebase and MLOps practices, mentor engineers and support client personnel in developing the capabilities required for future ownership of the solution.

Key Responsibilities

1. Technical Leadership and Build Delivery

• Lead the end-to-end engineering build-and-deploy effort for enterprise AI and agentic solutions.

• Supervise engineers and coordinate implementation activities to ensure alignment with approved architecture, design patterns and development standards.

• Translate architectural designs into actionable engineering tasks, implementation plans and technical deliverables.

• Monitor development progress, identify technical dependencies and resolve implementation challenges.

• Ensure solutions are delivered with the required quality, reliability, security and maintainability.

• Remain hands-on by developing critical components, resolving complex technical issues and establishing reusable reference implementations.

2. Architecture Collaboration and Technical Governance

• Act as the primary technical engineering counterpart to Microsoft, McKinsey and other partner architects.

• Understand, evaluate and challenge proposed architectural designs, technical decisions and implementation approaches constructively.

• Ask the right technical questions, seek clarifications and identify gaps, assumptions, dependencies and build-level risks.

• Ensure engineering teams understand and implement the intended architecture, patterns and standards.

• Validate technical feasibility and highlight potential issues relating to scalability, integration, security, performance and operational readiness.

• Communicate implementation risks, design concerns and technical recommendations clearly to partner and client stakeholders.

3. LLM and Agentic AI Engineering

• Design, develop and deploy production-grade LLM-powered applications and agentic AI systems.

• Implement LLM orchestration, multi-step workflows, retrieval-augmented generation (RAG) and tool/function calling.

• Develop integrations between AI models, enterprise data sources, business applications and external services.

• Implement agent workflows that support reliable task execution, context management and interaction with enterprise systems.

• Apply appropriate techniques for prompt engineering, retrieval optimisation, response validation and model interaction.

• Address challenges relating to AI system reliability, latency, scalability, security and production readiness.

4. Software Engineering Quality and Code Governance

• Establish and enforce engineering standards covering code quality, architecture, maintainability, testing and version control.

• Conduct code reviews and ensure adherence to secure coding practices and agreed development conventions.

• Implement unit, integration and end-to-end testing strategies for AI applications and supporting services.

• Establish systematic evaluation practices to assess AI output quality, accuracy, relevance, consistency and reliability.

• Ensure appropriate validation and testing of prompts, retrieval pipelines, agent behaviour and tool/function execution.

• Govern the codebase to maintain consistency, traceability, reusability and long-term maintainability.

5. MLOps, CI/CD and Deployment

• Own and govern MLOps practices supporting the development, testing, deployment and operation of AI solutions.

• Establish and maintain CI/CD pipelines for application components, AI workflows and associated deployment artefacts.

• Implement appropriate version control, environment management, configuration management and release practices.

• Support monitoring, logging, troubleshooting and continuous improvement of deployed AI systems.

• Ensure deployment processes incorporate appropriate testing, evaluation, security checks and release controls.

• Promote repeatable, reliable and auditable engineering processes across the AI development lifecycle.

6. Hands-on Development and Reusable Components

• Build core AI application components and reference implementations that can be reused across the engineering team.

• Develop modular, scalable and maintainable services using Python or other suitable programming languages.

• Establish reusable patterns for LLM orchestration, RAG pipelines, agent integrations, evaluation and deployment.

• Investigate and resolve complex technical issues across application, data, AI and integration layers.

• Evaluate implementation options and recommend solutions that balance technical quality, delivery speed and operational requirements.

7. Stakeholder Management and Delivery Risk

• Collaborate with partner architects, client stakeholders, engineering teams and project leadership.

• Communicate technical progress, delivery risks, dependencies and implementation decisions effectively.

• Identify and escalate risks that could affect delivery timelines, solution quality or production readiness.

• Support technical planning, estimation, prioritisation and resolution of engineering blockers.

• Ensure that implementation decisions remain aligned with business objectives and agreed architectural direction.

Required Skills & Experience

• 6–8 years of experience in software engineering, AI engineering or related technical disciplines, with strong and recent hands-on coding experience.

• Demonstrated experience building and deploying production-grade LLM applications and agentic AI systems.

• Strong programming proficiency in Python, or equivalent hands-on expertise in C#/.NET or TypeScript.

• Practical experience with LLM orchestration, retrieval-augmented generation (RAG), tool/function calling and agent workflows.

• Experience designing and implementing AI application components that integrate with enterprise systems and data sources.

• Strong software engineering fundamentals, including clean code, modular design, testing, version control and maintainable architecture.

• Experience establishing and enforcing code quality standards, conducting code reviews and governing a shared codebase.

• Strong understanding of CI/CD, deployment processes and MLOps practices for AI-enabled applications.

• Experience implementing testing and evaluation strategies for AI applications, including assessment of LLM and agent behaviour.

• Ability to understand, evaluate and constructively challenge architecture and design decisions proposed by external technical partners.

• Proven experience leading, supervising or technically guiding an engineering team through build and deployment activities.

• Ability to identify technical risks, clarify requirements and translate architectural designs into practical implementation plans.

• Strong communication and stakeholder management skills, including the ability to engage with senior technical stakeholders.

• Demonstrated ability to remain hands-on while providing technical leadership and maintaining delivery accountability.

Desirable Skills & Experience

• Experience with Microsoft Azure AI Foundry and Azure OpenAI Service.

• Familiarity with agentic AI frameworks such as LangChain, LangGraph, Semantic Kernel and AutoGen.

• Experience delivering AI solutions within a partner-led or vendor-led architecture.

• Experience working with Microsoft, consulting partners or external architecture teams on enterprise technology implementations.

• Knowledge of enterprise AI security, governance, responsible AI practices and compliance requirements.

• Experience in regulated environments, particularly compliance-intensive or manufacturing domains.

• Familiarity with cloud-native application development, containerisation, distributed systems and enterprise integration patterns.

• Experience with AI observability, evaluation frameworks, prompt management and production monitoring.

• Experience enabling client engineering teams through mentoring, shadowing, documentation and structured knowledge transfer.

• Understanding of enterprise data architectures, access controls and secure integration with internal business systems.

Conclusion – Ideal Candidate Profile

The ideal candidate will be a hands-on AI Engineering Developer with 6–8 years of software engineering experience and a proven track record of delivering production-grade LLM and agentic AI solutions. They should combine strong coding ability with sound engineering judgement, team leadership and the ability to translate partner-defined architecture into high-quality, production-ready implementations.

The successful candidate must be comfortable acting as the technical counterpart to senior partner architects, challenging design decisions constructively, identifying implementation risks and ensuring that engineering teams deliver against agreed architectural standards.

They should demonstrate strong expertise in LLM orchestration, RAG, tool/function calling, AI evaluation, code governance, CI/CD and MLOps. Equally important is the ability to remain hands-on, build reusable technical components, mentor engineers and enable client personnel to assume future ownership of the solution.

Candidates with experience in Azure AI Foundry, Azure OpenAI, agentic AI frameworks and vendor-led enterprise implementations will be highly regarded. Experience in compliance or manufacturing environments will be an added advantage.

Additional Requirements

• Core Technical Focus: Production LLM applications, agentic AI, RAG, orchestration, tool/function calling and evaluation.

• Programming Languages: Python preferred; strong C#/.NET or TypeScript experience is also acceptable.

• Engineering Focus: Code quality, testing, code reviews, version control, CI/CD, secure coding and MLOps governance.

• Stakeholder Engagement: Ability to work directly with Microsoft, McKinsey, partner architects and client engineering teams.