Senior Forward Deployed Engineer (Pod Lead)

Overview

Digile’s AI Practice is currently looking for a Senior Forward Deployed Engineer (Senior FDE) to lead one of our Forward Deployed Engineer (FDE) Pods, a customer‐embedded, cross‐functional AI delivery unit that converts AI capabilities into production‐grade workflows generating measurable business impact. The ideal candidate brings a blend of strong skillsets as requested in the section below and a strong passion for taking AI systems from demo to pilot, production and scale.

The main job responsibility of the Senior FDE is to act as the technical lead, solution architect and customer‐embedded delivery owner of the pod. As the Pod Lead, you hold technical decision rights for the pod and will work closely with the AI Product Manager, Junior FDEs, Domain SMEs and DevOps/MLOps Engineers to design and deliver AI workflows that integrate with real customer systems. You will be the primary technical interface with customer CTOs and Tech Leads. You must be capable of working independently, thinking strategically and having fun along the way!

Leading rapid prototyping (typically 1–2 weeks) to build functional prototypes and validate feasibility with real customer data.
Leading the design and build of Retrieval‐Augmented Generation (RAG) pipelines, systems integration with customer APIs and legacy systems, and AI guardrails and safety controls.
Ensuring production reliability of deployed workflows in partnership with the DevOps/MLOps Engineer, including stability targets for uptime, drift and error rates.
Owning production rollout and leading the technical scaling of solutions to additional use cases.
Mentoring and coaching Junior FDEs through code and prompt reviews, pairing and technical guidance as they transition into the FDE role.
Interfacing with customer CTOs and Tech Leads, and jointly owning customer alignment with the AI Product Manager.
Leading the weekly technical sync and monthly architecture review, and contributing to weekly delivery reports, monthly impact reviews and quarterly roadmap alignment.
Ensuring every workflow is deployable, monitorable and governable, with guardrails, auditability and compliance built in by design.
Maintaining technical competency and remaining current in AI technology and changes in the industry.
Willing to travel and be stationed at customer sites, within and outside of Kuala Lumpur, Malaysia.

EXPERIENCE:

6+ years of experience in software engineering, solution architecture or technical consulting, with at least 2 years delivering AI/ML or LLM‐based solutions into production.
Proven experience leading technical delivery in customer‐facing environments, e.g. as a Tech Lead, Solution Architect or Forward Deployed Engineer.
Hands‐on experience building RAG pipelines, vector databases and LLM‐based applications for enterprise use.
Experience integrating with enterprise systems (e.g. ERP, CRM, document management or legacy platforms) via APIs.
Experience mentoring or leading a team of engineers.
Experience delivering AI solutions in regulated industries such as financial services, public sector, healthcare or infrastructure will be a plus point.

SKILLS:

Strong proficiency in Python; TypeScript or C#/.NET will be a plus point.
Deep understanding of LLMs, prompt engineering, embeddings and agentic/tool‐use orchestration, including retrieval‐grounded (RAG) answers with source attribution, confidence indicators and evaluation datasets.
Experience with LLM frameworks such as LangChain, LlamaIndex or equivalent, and vector databases such as pgvector, Pinecone, Weaviate or Azure AI Search.
Hands‐on experience with Autodesk Construction Cloud (ACC) / Autodesk Forma and Autodesk Platform Services APIs, and with CxAlloy, including building bidirectional integrations through native connectors, ACC Connect or custom APIs.
Good understanding of BIM models, model validation and clash detection, and of EPC engineering deliverables such as PFDs, P&IDs, specifications, datasheets, equipment lists, electrical load lists and shop drawings, to architect AI‐assisted design verification.
Familiarity with Primavera P6 data (XER/XML/API), critical path and schedule logic, to design read‐only schedule intelligence that never modifies the approved baseline.
Strong data engineering skills, including SQL, ETL, REST APIs and semantic data modelling, to build a common project intelligence model across schedule, cost, procurement/OFCI, quality and commissioning data.
Strong knowledge of human‐in‐the‐loop AI governance and guardrails, including evidence‐backed outputs, SSO, RBAC, audit trails, data lineage and ensuring client data is not used for model training.
Working knowledge of at least one major cloud platform (Microsoft Azure, AWS or Google Cloud) and its AI services, as well as containers and CI/CD.
Strong ability to present architecture and trade‐offs to client engineering leadership, IT/security teams and our leadership team in a manner that is clear, concise and open.
Excellent written and verbal communication skills.

AI-First Enterprise Application Strategy for 2027: From Managi...

Building a Connected Oracle Ecosystem for a Global Data Center...