AI SDLC Forward Deployed Engineer - Consultant

Experience

• 2–4 years of experience in a technical engineering role with client-facing or consulting exposure — Software Engineer, Solutions Engineer, technical consultant, or equivalent.
• Experience delivering production applications with real business impact, ideally in enterprise environments.
• Practical experience working with large language models (ideally in a enterprise or start-up setting): prompt engineering, loop engineering, skill creation, agent development and use, evaluation frameworks, workflows, and deployment.
• Hands-on experience building and deploying with AI agents and workflows.
• Working familiarity with cloud platforms (AWS, Azure, GCP), and system integration patterns and CI/CD.
• Consulting & Client Skills
• High agency; comfortable navigating ambiguity, working across complex organisations, and contributing without a well-defined playbook.
• Strong communication skills; able to participate in technical discovery, support stakeholder alignment, and translate AI concepts for non-technical audiences.
• Technically minded and curious. You pick up new tools and methods quickly, understand how systems fit together, and can test an idea by building a small working version.
• Domain exposure in media, or another enterprise vertical.
• Familiarity with AI safety, responsible AI frameworks, or enterprise AI governance.
• Familiarity with AI deployment infrastructure and MLOps tooling.

Technical Skills

• Solid programming proficiency (we are open to any experience but primary languages include: TypeScript and the JavaScript ecosystem, Python and Java). However, flexibility with languages and tools is important with our clients wanting to move to tech stacks including Go, Rust etc. A willingness to go deep into unfamiliar tech stacks is preferred.
• Hands-on experience building and deploying with AI agents and workflows.
• Working familiarity with cloud platforms (AWS, Azure, GCP), and system integration patterns and CI/CD.
• Consulting & Client Skills
• High agency; comfortable navigating ambiguity, working across complex organisations, and contributing without a well-defined playbook.
• Strong communication skills; able to participate in technical discovery, support stakeholder alignment, and translate AI concepts for non-technical audiences.
• Technically minded and curious. You pick up new tools and methods quickly, understand how systems fit together, and can test an idea by building a small working version.
• Preferred
• Domain exposure in media, or another enterprise vertical.
• Familiarity with AI safety, responsible AI frameworks, or enterprise AI governance.
• Familiarity with AI deployment infrastructure and MLOps tooling.

About Accenture

Preferred Qualifications

• Solid programming proficiency (we are open to any experience but primary languages include: TypeScript and the JavaScript ecosystem, Python and Java). However, flexibility with languages and tools is important with our clients wanting to move to tech stacks including Go, Rust etc. A willingness to go deep into unfamiliar tech stacks is preferred.
• Preferred

Responsibilities

• Deliver production-quality work — Build and ship applications inside client systems, using AI SDLC workflows and processes ensuring solutions meet enterprise requirements for performance, security, and reliability.
• Develop technical assets — Create reusable, production-ready artefacts or tools including MCP servers, sub-agents, agent orchestration frameworks, and evaluation tooling that can be deployed across client workflows.
• Support enterprise deployments — Play a key technical role in rollouts of frontier AI capabilities, in particular AI in the SDLC processes, ensuring smooth integration with existing IT infrastructure, workflows applications.
• Accelerate adoption — Work shoulder-to-shoulder with client engineering and product teams to drive meaningful AI adoption, removing blockers and closing skill gaps in real time. This will include up-skilling people at Accenture and our clients with AI centric engineering.
• Maintain cutting-edge expertise — Stay ahead of the curve on LLM capabilities, with a focus on the SDLC, agentic frameworks, evaluation methodologies, and the AI product development ecosystem.
• Build strong relationships — Develop trusted working relationships with client teams and AI ecosystem partners, proactively contributing to new opportunities for AI value creation throughout the engagement lifecycle.
• Represent Accenture in the field — Champion responsible, safe AI usage and uphold Accenture's standards in every client interaction.
• What We're Looking For