Forward Deployed Architect – Agentic AI

Overview

The Forward Deployed Architect – Agentic AI serves as the lead technical authority for enterprise agentic AI engagements. This role is responsible for defining, designing, and guiding the implementation of enterprise-grade agentic AI solutions from architectural conception through production deployment.

The position focuses on building scalable, reliable, and governable multi-agent systems that operate effectively within regulated enterprise environments. Responsibilities include defining orchestration architectures, enterprise integrations, human-in-the-loop processes, evaluation frameworks, governance models, and operational monitoring strategies.

Working closely with client stakeholders, Architects, Engineers, Strategists, and Delivery teams, the Forward Deployed Architect ensures technical decisions align with business objectives, operational requirements, and enterprise governance standards.

This is a highly hands-on role requiring deep expertise in agentic AI systems, AWS architecture, enterprise integrations, security, and large-scale production delivery.

The client encourages all team members to leverage AI tools in their daily work. AI assistants and workflow automation solutions are expected to enhance architecture design, technical reviews, evaluation validation, engineering productivity, and solution development activities.

Key Responsibilities

Own the end-to-end architecture for enterprise agentic AI engagements.
Design and implement multi-agent systems on AWS and the client's AI platform, including orchestration patterns, request routing, memory strategies, retrieval architectures, observability, guardrails, and evaluation frameworks.
Design human-in-the-loop workflows suitable for regulated industries such as banking, insurance, and telecommunications.
Define governance frameworks, review gates, explainability mechanisms, and operational controls required for enterprise AI deployments.
Leverage AWS Bedrock, Agent Core, leading AI model providers, orchestration frameworks, and evaluation tooling to build scalable and production-ready AI solutions.
Assess architectural trade-offs across AI models, frameworks, deployment approaches, integrations, and infrastructure designs.
Design integrations with enterprise systems including APIs, hybrid environments, legacy platforms, identity services, and regulated infrastructure environments.
Define evaluation methodologies, testing strategies, observability frameworks, drift detection mechanisms, and production monitoring models for agentic AI systems.
Review engineering deliverables to ensure architecture quality, scalability, security, reliability, and operational readiness.
Produce architecture decision records (ADRs), technical design documentation, governance artefacts, and reusable design patterns.
Support pre-sales activities through technical workshops, architecture discussions, and solution-definition sessions with enterprise stakeholders.
Contribute to estimation, sprint planning, work breakdown structures, backlog management, and technical governance across engagements.
Develop reusable intellectual property including reference architectures, governance frameworks, orchestration models, and evaluation methodologies that strengthen the client's AI delivery capability.

Requirements

10+ years of experience in software engineering, AI/ML systems, cloud architecture, or enterprise technology delivery environments.
3–5+ years of hands-on experience designing and deploying production AI or agentic systems within enterprise environments.
Deep expertise in AWS architecture and cloud-native delivery, including Bedrock, Agent Core, SageMaker, Lambda, API Gateway, containerization, serverless computing, observability, and enterprise security services.
Strong knowledge of multi-agent orchestration, Retrieval-Augmented Generation (RAG), prompt engineering, tool integration design, MCP server architectures, evaluation frameworks, and agentic AI failure modes.
Familiarity with modern AI ecosystems, model providers, orchestration frameworks, and deployment methodologies.
Experience integrating AI solutions into enterprise environments with complex security, networking, governance, identity, and compliance requirements.
Strong understanding of enterprise integration concepts including APIs, event-driven architectures, IAM, VPC design, PrivateLink, identity federation, access management, and data governance.
Experience working within BFSI, telecommunications, insurance, or other highly regulated industries.
Strong understanding of AI security risks including prompt injection, data leakage, access controls, secrets management, encryption, and auditability requirements.
Ability to establish production reliability standards, evaluation methodologies, observability strategies, and operational governance frameworks for enterprise AI systems.
Excellent executive communication skills with the ability to explain technical trade-offs, AI limitations, governance controls, and operational risks to both technical and business stakeholders.
Strong technical leadership across engineering teams, delivery execution, technical governance, and programme management.

Preferred Attributes

Proven experience designing and deploying enterprise-grade agentic AI systems within regulated environments.
Experience navigating governance, risk, compliance, and operational requirements for production AI deployments.
Active use of AI tools to improve architecture analysis, evaluation design, engineering reviews, and solution development workflows.
Contributions to reusable architecture frameworks, governance models, or evaluation methodologies adopted across multiple teams or engagements.
Ability to combine deep technical expertise with strong stakeholder engagement and business communication skills.

Travel & Location

Our clients span Southeast Asia, ANZ, and Japan. The role requires close collaboration with client teams and may involve travel or in-country presence depending on engagement requirements. Relocation assistance may be considered based on business needs.

Success Measures

Success in this role will be demonstrated through:

Enterprise clients successfully deploy agentic AI solutions that meet business, operational, and governance objectives.
Agentic systems consistently operating within defined evaluation benchmarks and production governance standards.
Delivery teams adopting reusable architecture patterns, governance frameworks, and evaluation models created through engagements.
Enterprise stakeholders maintaining confidence in the architecture, governance controls, and operational reliability of deployed solutions.
Continuous enhancement of the client's enterprise AI delivery capabilities through reusable intellectual property and delivery excellence.

Language Requirements

Fluency in spoken and written English is required.
Strong communication skills are essential for collaborating with enterprise stakeholders, executives, technical teams, and cross-regional delivery teams.
Additional language proficiency is advantageous, particularly Japanese, Vietnamese, Thai, or Filipino.

Certifications

Required:

AWS Certified Solutions Architect – Professional
AWS Certified Machine Learning Specialty or AWS Certified Machine Learning Engineer – Associate

Preferred:

AWS Certified Generative AI Developer
AWS Certified Security Specialty

What We Offer

Competitive compensation aligned with market benchmarks.
Opportunities to work on enterprise-scale AI transformation programmes across the Asia-Pacific region.
Exposure to advanced AI technologies, AWS partner ecosystems, regulated enterprise environments, and high-impact production AI initiatives.

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