Forward Deployed Engineer, UIUX (Contract)

ARGYLL SCOTT CONSULTING PTE. LTD.; Singapore; On-site; SGD 120,000 - 180,000; Full time

Job summary

ARGYLL SCOTT CONSULTING PTE. LTD. is seeking a Forward Deployed Engineer for a 12‑month contract in Singapore. You will partner with business units to translate workflows into AI-enabled, production-ready solutions, delivering rapid prototypes and end-to-end handover.

You will build AI workflows, backend services, and lightweight UIs, while ensuring security, governance and observability. This role requires 5+ years in software engineering and strong collaboration with non-technical users.

Qualifications

• 5+ years of software engineering experience including ownership of production applications from discovery to deployment.
• Backend engineering: proficiency in Python or another modern backend language, API design, asynchronous processing, integration patterns, testing, debugging, and system architecture.
• Database & data engineering: strong experience with relational databases (preferably PostgreSQL), data modelling and query optimisation, plus data pipelines, search and vector retrieval.
• GenAI & agentic systems: hands-on experience building LLM-enabled applications, RAG solutions, tool-calling agents or automated workflows with LangGraph, LangChain, LlamaIndex, n8n or equivalent.
• Cloud & production delivery: experience deploying and operating cloud-native apps; familiarity with AWS Bedrock and OpenSearch is preferred (Azure/GCP acceptable).
• Stakeholder partnership: strong communication and facilitation with non-technical users, clarifying needs, trade-offs, and adoption.
• Delivery mindset: comfortable in ambiguous environments, quickly learning new domains, balancing value, speed, security and maintainability.

Responsibilities

• Embed with business units to understand workflows, pain points, data and outcomes; translate needs into prioritized use cases and stories.
• Build prototypes quickly, validate with users, and iterate toward production deployment.
• Design and implement AI workflows and agents using Python-based frameworks and orchestration tools.
• Develop backend services, APIs, integrations, authentication, and lightweight UIs as needed.
• Design robust data ingestion, transformation and retrieval pipelines; integrate enterprise systems and APIs.
• Develop secure, scalable backend data models and APIs using PostgreSQL; ensure data quality and access controls.
• Define evaluation datasets and acceptance criteria; test quality, latency, cost, and observability; improve prompts and retrieval.
• Apply security, governance and responsible AI practices with oversight and auditing.
• Own deployment readiness, testing, rollout, documentation and handover; track adoption and outcomes.

Skills

Backend engineering

Database & data engineering

GenAI & agentic systems

Cloud & production delivery

Stakeholder partnership

Delivery mindset

Tools

LangGraph

LangChain

LlamaIndex

n8n

Flowise

Bedrock

OpenSearch

AWS

Azure

GCP

Job description

Forward Deployed Engineer, UIUX (Contract)

Location: Singapore

Contract Type: 12 months contract (Extendable & Convertible)

Company/Role Overview

We are looking for an experienced, product-minded Engineer to work directly with business units and use our established enterprise GenAI platform to deliver high-impact AI and automation solutions. In this forward-deployed role, you will partner closely with business users to understand their workflows, translate often ambiguous needs into well-scoped solutions, and own delivery from discovery and rapid prototyping through production rollout, adoption, and handover.

Key Responsibilities

• Business Discovery & Solution Shaping: Embed with business units to understand operational workflows, pain points, data, constraints, and intended outcomes. Facilitate discovery sessions, challenge assumptions, and translate needs into prioritised use cases, user stories, solution designs, and measurable success criteria.
• Rapid Prototyping & Iterative Delivery: Build working prototypes quickly, validate them with users, and iterate based on evidence. Balance speed with maintainable engineering and take viable solutions through production deployment.
• Agentic Workflow Engineering: Design and implement reliable AI workflows and agents using Python-based frameworks and orchestration tools such as LangGraph, LangChain, n8n, or equivalent technologies. Combine deterministic steps, LLM reasoning, tool calling, state management, human approvals, and exception handling as appropriate.
• Custom Application Development: Build fit-for-purpose applications around business use cases, including backend services, APIs, integrations, authentication, workflow logic, and lightweight user interfaces where needed.
• Data & Knowledge Integration: Design robust data ingestion, transformation, and retrieval pipelines. Integrate enterprise systems, databases, document repositories, APIs, and search or vector services while maintaining data quality, lineage, and access controls.
• Backend & Database Engineering: Develop secure, scalable backend services and APIs, and design relational data models using technologies such as Python, FastAPI, and PostgreSQL. Diagnose complex integration, performance, and state-management issues.
• AI Quality, Evaluation & Observability: Define evaluation datasets and acceptance criteria; test solution quality, reliability, latency, cost, and tool-call success; implement tracing and monitoring; and continuously improve prompts, retrieval, workflows, and safeguards.
• Security, Governance & Responsible AI: Apply secure engineering practices, least-privilege access, data protection, prompt-injection defences, output controls, auditability, and human oversight in line with government policies and the risk profile of each use case.
• Deployment, Adoption & Handover: Own production readiness, testing, rollout, documentation, user enablement, support, and knowledge transfer. Track adoption and business outcomes and incorporate field learnings into reusable patterns and improvements to the core platform.

Job Requirements

Essential Requirements

• Experience: 5+ years of software engineering experience, including demonstrated ownership of production applications from ambiguous requirements through deployment and support.
• Backend Engineering: Strong proficiency in Python or another modern backend language, API design, asynchronous processing, integration patterns, testing, debugging, and system architecture.
• Database & Data Engineering: Strong experience with relational databases, preferably PostgreSQL, including data modelling and query optimisation; practical knowledge of data pipelines, search, and vector retrieval is also required.
• GenAI & Agentic Systems: Hands-on experience building LLM-enabled applications, RAG solutions, tool-calling agents, or automated workflows using frameworks such as LangGraph, LangChain, LlamaIndex, n8n, or equivalent.
• Cloud & Production Delivery: Practical experience deploying and operating cloud-native applications. Familiarity with AWS and services such as Bedrock and OpenSearch is preferred, although equivalent Azure or GCP experience is acceptable.
• Stakeholder Partnership: Strong communication and facilitation skills, with the ability to work directly with non-technical users, clarify business needs, explain trade-offs, manage expectations, and drive adoption.
• Delivery Mindset: Comfortable working in ambiguous environments, learning unfamiliar business domains quickly, and balancing user value, delivery speed, security, reliability, and maintainability.

Additional Attributes

• Forward Deployment: Experience in consulting delivery, solutions engineering, internal product delivery, or another role involving direct collaboration with users and end-to-end implementation.
• Workflow Automation: Experience with visual workflow builders such as n8n or Flowise, including custom nodes, integrations, cr