The Forward Deployed AI Engineer embeds directly within active client and internal project teams to drive real-world adoption of AI engineering practices. Working as the delivery-facing counterpart to our internal AI R&D function, the role assesses each project's context, implements AI tools and agentic solutions where they create measurable value, and enables teams to sustain these practices independently after rollout.

Responsibilities

Assess project context and needs prior to AI rollout, identifying where AI practices, tools, or agentic solutions create measurable value

Translate project and business needs into actionable AI adoption opportunities and drive them from initial assessment through implementation and operationalization

Help delivery teams integrate AI practices into their existing SDLC across development, testing, QA, and operational workflows

Facilitate workshops, onboarding, and enablement sessions with engineers and project stakeholders

Implement or adapt reference solutions and accelerators; provide post-rollout support and resolve adoption or technical issues

Measure adoption, collect feedback, and feed lessons learned back into the AI R&D function and the AI SDLC

Contribute reusable rollout playbooks, patterns, documentation, and guidance

Operate across PO, PM, engineering, QA, and operational concerns as required by the rollout context, without becoming a permanent dependency for the project team

Must Have

Strong software engineering fundamentals and software delivery lifecycle experience: architecture, APIs, testing, delivery practices, maintainability, and production software; ability to quickly understand unfamiliar systems and transfer principles across languages and stacks

Hands-on experience with AI-assisted software development (AI SDLC)

Proven ability to understand and extract business and engineering requirements; consulting mindset: stakeholder interviewing, challenging assumptions, proposing pragmatic solutions

Hands-on experience with AI agents and agentic workflows

Experience with LLM APIs and model integration

Experience with CI/CD and modern software delivery practices

Practical understanding of evaluation and testing of AI systems

Strong software quality and engineering practices

Production support and troubleshooting experience

Awareness of security, privacy, and responsible AI considerations

Experience with observability and monitoring

Strong communication skills across technical and non-technical audiences; comfort with frequent context switching; high ownership and autonomy; ability to operate with ambiguity

Nice to Have

LLM-based application development

Retrieval-Augmented Generation (RAG)

Tool/function calling and protocols such as MCP

API design and integration

Data pipelines and data management

Containers and cloud-native development

Experience with AI coding assistants and AI-enabled development workflows

Experience with Python, JavaScript/TypeScript, Java, .NET, or similar (specific languages are not a primary selection criterion)

As a people-first organisation, we believe diversity strengthens our teams and drives innovation. All employment decisions are based on merit, skills, and performance, without discriminating based on any personal characteristic. This reinforces our commitment to providing an inclusive and respectful workplace.

At Accesa you canEnjoy our holistic benefits program that covers the four pillars that we believe come together to support our wellbeing, covering social, physical, emotional wellbeing, as well as work-life fusion.

Physical Wellbeing: Our wellbeing program includes medical benefits, gym support, and personalised fitness options for an active lifestyle, complemented by team events and the Healthy Habits Club.

Work-Life Fusion: In very dynamic industries such as IT, the line between our professional and personal lives can quickly become blurred. Having a one...