AWS Pre-Sales Engineer AI & Data Apac
Summary
A client-embedded AWS Pre-Sales Engineer focused on AI/data transformation, designing AWS-based solutions, building live demos, and leading deal origination for enterprise clients.
We are seeking a Senior Data & AI Presales Engineer to act as a Forward Deployed Engineer (FDE)—working directly with clients to originate, shape, and win AI & data transformation opportunities.
This is a high-impact, hands-on, client-embedded role requiring the ability to:
Engage clients to identify and shape high-value AI/data use cases
Design and build AWS-based solutions aligned to business outcomes
Develop live demos and prototypes on the spot to accelerate deal conversion
Lead small teams to deliver end-to-end solutioning and pre-sales execution
The ideal candidate combines strong engineering capability, presales instincts, and rapid prototyping expertise.
Key Responsibilities
Forward Deployed Engineering (Client-Embedded Role)
Work directly with client stakeholders (business + IT) to:
Identify use cases and problem statements
Validate technical feasibility and solution fit
Act as a technical co-pilot during deal shaping, iterating solutions in real time
Rapidly adapt solutions based on:
Client feedback
Data availability
Business constraints
Expected behavior:
Build, test, and refine solutions in the client environment and context
Client Engagement & Deal Origination
Lead:
Discovery workshops
Technical solution discussions
Architecture deep-dives
Translate business challenges into:
AI/data use cases
Executable solution designs
Support:
Opportunity creation and qualification
Proposal development and technical response
Demo Engineering & Rapid Prototyping (Critical)
Build live demos, PoCs, and proof-of-value solutions during client engagements
Translate concepts into:
Working applications
API-driven services
AI-enabled workflows
Expected capability:
Build a functional demo within hours/days, such as:
GenAI assistants (RAG-based chatbots)
AI-powered document processing (KYC, contracts)
Customer analytics / recommendation engines
AWS Data & AI Engineering (Hands-on)
Data Engineering Design and build:
Data lakes and pipelines (S3, Glue, Athena)
Data warehousing solutions (Redshift, Aurora)
Work with structured and unstructured enterprise data
AI / ML / GenAI
Develop and integrate:
Machine learning models (SageMaker)
GenAI applications (Bedrock, LLM-based solutions)
Implement:
RAG pipelines
Vector search and knowledge retrieval
Application Development
Build cloud-native applications using:
Lambda, ECS/EKS
API Gateway, Step Functions
Deliver end-to-end working solutions, not just architecture
Solutioning & Architecture Leadership
Define:
Solution architecture and patterns
Technology stack and integration approach
Ensure:
Scalability, performance, and cost optimization
Drive solution alignment with:
Client KPIs and business outcomes
Team Leadership & Delivery Coordination
Lead small technical teams for:
Demo development
Solutioning and proposal support
Provide:
Technical direction and quality assurance
Collaborate with:
Architects, data scientists, and delivery teams
Required Qualifications
4-8+ years of experience in:
Data engineering, AI/ML, or cloud engineering
Enterprise or financial services environments
Strong hands-on experience in:
AWS data and AI technologies
Designing and building cloud-based solutions
Proven experience in:
Client-facing presales / solutioning roles
Engaging clients to shape and win deals
Strong hands-on programming skills in Python, with ability to build:
Data pipelines
APIs and cloud-native applications
AI/ML and GenAI prototypes
Demonstrated ability to:
Build live demos / PoCs under time pressure
Translate ideas into working solutions quickly
Preferred Qualifications
Experience in:
BFSI (banking, insurance, financial services)
AI use cases (fraud, risk, personalization, customer 360)
Hands-on experience with:
GenAI frameworks (RAG, LangChain, orchestration tools)
Streaming data / real-time processing
AWS Certifications:
Data Engineer / Machine Learning / Solutions Architect
Key Success Profile
Forward Deployed Engineer mindset — thrives in client-facing, fast-moving environments
Strong builder mentality — able to code, prototype, and demo live
Strong presales instinct — can originate and shape deals
Able to bridge business needs with technical solutions
Comfortable operating in ambiguity and rapid iteration cycles
This is a high-impact, hands-on, client-embedded role requiring the ability to:
Engage clients to identify and shape high-value AI/data use cases
Design and build AWS-based solutions aligned to business outcomes
Develop live demos and prototypes on the spot to accelerate deal conversion
Lead small teams to deliver end-to-end solutioning and pre-sales execution
The ideal candidate combines strong engineering capability, presales instincts, and rapid prototyping expertise.
Key Responsibilities
Forward Deployed Engineering (Client-Embedded Role)
Work directly with client stakeholders (business + IT) to:
Identify use cases and problem statements
Validate technical feasibility and solution fit
Act as a technical co-pilot during deal shaping, iterating solutions in real time
Rapidly adapt solutions based on:
Client feedback
Data availability
Business constraints
Expected behavior:
Build, test, and refine solutions in the client environment and context
Client Engagement & Deal Origination
Lead:
Discovery workshops
Technical solution discussions
Architecture deep-dives
Translate business challenges into:
AI/data use cases
Executable solution designs
Support:
Opportunity creation and qualification
Proposal development and technical response
Demo Engineering & Rapid Prototyping (Critical)
Build live demos, PoCs, and proof-of-value solutions during client engagements
Translate concepts into:
Working applications
API-driven services
AI-enabled workflows
Expected capability:
Build a functional demo within hours/days, such as:
GenAI assistants (RAG-based chatbots)
AI-powered document processing (KYC, contracts)
Customer analytics / recommendation engines
AWS Data & AI Engineering (Hands-on)
Data Engineering Design and build:
Data lakes and pipelines (S3, Glue, Athena)
Data warehousing solutions (Redshift, Aurora)
Work with structured and unstructured enterprise data
AI / ML / GenAI
Develop and integrate:
Machine learning models (SageMaker)
GenAI applications (Bedrock, LLM-based solutions)
Implement:
RAG pipelines
Vector search and knowledge retrieval
Application Development
Build cloud-native applications using:
Lambda, ECS/EKS
API Gateway, Step Functions
Deliver end-to-end working solutions, not just architecture
Solutioning & Architecture Leadership
Define:
Solution architecture and patterns
Technology stack and integration approach
Ensure:
Scalability, performance, and cost optimization
Drive solution alignment with:
Client KPIs and business outcomes
Team Leadership & Delivery Coordination
Lead small technical teams for:
Demo development
Solutioning and proposal support
Provide:
Technical direction and quality assurance
Collaborate with:
Architects, data scientists, and delivery teams
Required Qualifications
4-8+ years of experience in:
Data engineering, AI/ML, or cloud engineering
Enterprise or financial services environments
Strong hands-on experience in:
AWS data and AI technologies
Designing and building cloud-based solutions
Proven experience in:
Client-facing presales / solutioning roles
Engaging clients to shape and win deals
Strong hands-on programming skills in Python, with ability to build:
Data pipelines
APIs and cloud-native applications
AI/ML and GenAI prototypes
Demonstrated ability to:
Build live demos / PoCs under time pressure
Translate ideas into working solutions quickly
Preferred Qualifications
Experience in:
BFSI (banking, insurance, financial services)
AI use cases (fraud, risk, personalization, customer 360)
Hands-on experience with:
GenAI frameworks (RAG, LangChain, orchestration tools)
Streaming data / real-time processing
AWS Certifications:
Data Engineer / Machine Learning / Solutions Architect
Key Success Profile
Forward Deployed Engineer mindset — thrives in client-facing, fast-moving environments
Strong builder mentality — able to code, prototype, and demo live
Strong presales instinct — can originate and shape deals
Able to bridge business needs with technical solutions
Comfortable operating in ambiguity and rapid iteration cycles