AI Technical Consultant

The AI Tech Consultant operates at the intersection of high-level technical architecture and strategic client advisory, serving as the technical advisor within the AI Insight & Strategy Department.

While the AI Analyst focuses on diagnosing the business “Why” and “What”, your focus is on executing project-specific technical data audits and mapping initial technical constraints. Your mission is to pre-evaluate and technically de-risk the AI strategic transformation blueprints designed for clients by coordinating directly with downstream DSS Solution Architects (SAs) and Technical Architects (TAs) who own and validate the technical ‘How’ and ‘Can We’. Operating entirely within the pre-production and advisory ecosystem, you will translate high-level client intent into structured readiness assessments. This ensures a seamless, high-quality handover of strategic blueprints to DSS execution, preventing foundational technical debt without entering detailed solution design or production maintenance lifecycles.

KEY RESPONSIBILITIES

1. Discovery and Strategy to Frame Technical Feasibility

• Technical Landscape & Constraint Identification: Collaborate with AI/ML Analysts and clients to deeply understand their existing data infrastructure, translating high-level business needs into strict technical requirements, data inputs, and system integrations.
• Feasibility Evaluation & Tech Stack Alignment: Assess the technical feasibility of proposed AI/ML use cases, ensuring alignment with the organization’s vetted tech stack and internal standards.
• Technical Data Audit & Integration Mapping: Apply established data maturity frameworks to conduct hands-on, project-specific technical audits of client data (evaluating actual schema quality, API accessibility, and technical pipeline constraints) to map out the exact technical prerequisites for AI/ML execution.
• Application-Layer & Strategy Technology Exploration: Proactively research and explore application-layer AI tools, methodologies, and frameworks (e.g., innovative agentic architectures, RAG design patterns) to discover next-generation advisory capabilities, leveraging these insights to review the idea backlog and enrich strategic planning.

2. Inception Framework Delivery (Technical Validation)

• Architectural Vision & Scope: Establish the initial technical blueprint and architectural considerations for the project, defining technical “In” vs. “Out” boundaries to minimise technical debt.
• Prototyping Collaboration & Strategy Validation: Partner closely with the Data Science Solutions (DSS) department’s Solution Architects and AI Engineers to realize rapid, interactive proof-of-concepts (PoCs). Serve as the crucial validation point to ensure that the DSS-proposed technical direction and overall AI strategy align perfectly with the client’s business goals, while conceptually safeguarding production scalability, security, and pre-production technical debt reduction.
• Technical Trade-off Sliders: Guide critical decision-making by evaluating technical constraints (e.g., latency vs. accuracy, compute costs vs. performance) to keep the project realistic and aligned with budgets.
• Technical Roadmap & Prerequisites: Formulate the technical “What’s Next” implementation plans, outlining the technical roadmaps required to enhance client data capabilities for the proposed AI solution.

3. Client Facing & Approaches

• Technical Value Articulation: Simplify complex AI/ML concepts, algorithms, and technical architectures into understandable, value-driven terms for non-technical business stakeholders during client workshops.
• Prototype Demonstrations: Lead compelling technical demonstrations and visualizations of prototypes to showcase the functionality and immediate impact of proposed solutions, iterating rapidly based on client feedback.
• Production Handover & Collaboration: Work in close partnership with the downstream data science and/or production teams. Ensure all initial technical elicitation is documented according to Enablement Quality Gate standards to facilitate flawless technical handovers and minimize rework.
• Stakeholder Alignment: Present architectural considerations, prototype results, and technical discovery findings to both client leadership and internal strategy teams.
• Emerging Tech Advocacy: Support the team lead by testing application-layer use cases for new tools and frameworks, feeding real-world performance data back into strategic planning. Provide direct feedback on the performance of vetted tools to support strategic planning and client education.
• Client Satisfaction & Advisory Excellence (CSAT): Accountable for delivering high-quality client workshops and advisory engagements, ensuring that clear communication and technical de-risking drive top-tier Customer Satisfaction (CSAT) scores for the Insight & Strategy track.

QUALIFICATIONS & SKILLS

Education & Experience

• Academic Background: Minimum Bachelor’s degree in Computer Science, Data Science, Engineering, Mathematics, Statistics, or a related quantitative field. Master’s or Ph.D. preferred.
• Professional Track Record: At least 3+ years of progressive experience in AI/ML solution design, data science, or AI engineering, with a significant portion spent in a client-facing or consulting capacity.
• Consulting Origin: Prior professional experience working within a technology or management consultancy firm.
• Technical Leadership: Experience leading junior members and making high-level technical recommendations that directly drive project success.

Core Technical Competencies & “Must-Haves”

• Technical Liaison: Exceptional ability to translate complex AI architectures and algorithms into clear business language for non-technical clients and managers.
• Hands-on Prototyping & AI/ML Depth: Deep technical knowledge and hands-on experience building rapid PoCs using traditional ML and modern Generative AI frameworks (e.g., LLMs, AI Agents etc.).
• Agentic Frameworks & Orchestration: Practical experience with ReAct loops, Tool Calling, and Context Management. Proven understanding of how to prevent “context drift” in long-running agent sessions.
• Data Architecture Evaluation: Proven capability to audit and evaluate enterprise-level data maturity, infrastructure, and integration constraints.
• Agile & Iterative Mindset: Experience working in fast-paced consulting environments, rapidly pivoting prototypes based on client workshop feedback.

The “Bonus Points” (Career Background)

• Career background as a core Data Scientist (DS) or AI Engineer (AIE) looking to pivot into client-facing advisory track.
• Hands-on applied AI specialization experience deploying RAG architectures, orchestrating AI Agents, or navigating production MLOps tools.
• Experience using Miro, architectural mapping tools, or basic BI tools (Tableau, PowerBI) to visualize and communicate technical data flows and solution blueprints to stakeholders.

WHY YOU’LL LOVE WORKING WITH US

Flexible Hours: 5 working days/week (Mon-Fri) with a focus on output, not clock-watching.

Hybrid Model: 2 days WFH per week (team-based decision).

Generous Leave: 12 days annual leave + special seniority benefits.

Allowances: Lunch and gasoline support.

Competitive Income: 13th-month salary + Performance-based bonuses.

Full Insurance: Social, Health, and Unemployment insurance based on Gross Salary.

Premium Care: Health Insurance for you, with seniority-based extension to family members.

Learning Culture: Training for specialized skills, soft skills, and English.

Engagement: Monthly Happy Hours, Company Trips, and vibrant Sport Clubs (Soccer, Yoga, Badminton).

Well-being: Annual health check-ups and a supportive, collaborative environment.

Clear career progression in venturing into domain specific expertise with AI and business knowledge

Variety of choices of clients for you to interact with to grow and learn.