AI Lead - 6 Months Contract

Overview

Ai Solution Lead

Solution Architecture & Agentic Ai Design

• Own the
• end‑to‑end solution design
• covering:
• Agent orchestration and multi‑agent workflows
• RAG pipelines and tool‑use patterns
• Agent memory management (short‑term context, long‑term retrieval, episodic memory, and memory lifecycle policies)
• Design scalable, maintainable architectures that balance
• cost, latency, accuracy, and relevance
• .

Model Strategy & Build‑vs‑Buy Decisions

• Evaluate and select
• foundation models
• across:
• Cloud‑hosted platforms (e.g., Azure AI Foundry, AWS Bedrock)
• Locally hosted deployments (e.g., vLLMs)
• Make informed
• build‑vs‑buy decisions
• , considering:
• Performance and cost
• Deployment constraints
• Data sovereignty and regulatory needs
• Drive adoption of

Vision Language Models (Vlms)

Voice & Multi‑Modal AI Design

• Define the
• voice AI strategy
• , selecting and integrating STT/TTS solutions (e.g., OpenAI Whisper, Chatterbox, Vibe Voice, or equivalent).
• Ensure voice is treated as a
• first‑class interaction modality
• , deeply integrated into agent workflows.
• Design
• multi‑modal experiences
• that combine text, vision, and voice within a unified product.

Compliance, Privacy & Multi‑Jurisdiction Design

• Embed
• regulatory requirements by design
• (GDPR, PDPA, and local data regulations).
• Ensure solution designs support
• multi‑market deployments
• without retrofitting compliance later.
• Evaluate and apply
• privacy‑preserving techniques
• as appropriate.

Integration & Interoperability

• Define and align the
• integration architecture
• with group standards.
• Enable coordination between:
• Specialist agents (e.g., insurance resolution, health services, triage)
• External partner systems and third‑party tools
• Evaluate emerging
• agent interoperability standards
• (A2A, MCP, open tool‑use protocols) and sequence adoption based on delivery capacity and regulatory constraints.

Technical Leadership & Delivery

• Provide
• hands‑on technical leadership
• to external/vendor delivery teams.
• Translate product strategy and business OKRs into
• sequenced technical roadmaps
• .
• Review agent workflows, challenge assumptions, and catch architectural risks early.
• Balance innovation with BAU delivery.

Stakeholder Management

• Lead and present
• technical strategy, progress, and trade‑offs
• to CXOs and senior leadership.
• Drive alignment and decision‑making across product, engineering, and data teams.
• Proactively remove dependencies and delivery bottlenecks across squads.

Technical Requirements

• **5–7+ years**
• in solution design, technical product ownership, or senior applied AI roles.
• **Production experience**
• with agentic AI frameworks (e.g., AutoGen, CrewAI, PydanticAI, LangChain/LangGraph).
• Proven experience designing and shipping
• multi‑agent systems
• (beyond prototypes).
• Strong applied AI foundation:
• Traditional ML (model evaluation, feature design)
• Modern LLM patterns (RAG, prompt engineering, agent orchestration, agent memory design)
• Working knowledge of:
• **Vision Language Models (VLMs)**
• **Voice AI models**
• and integration patterns (STT/TTS)
• **AI observability and evaluation tools**

(e.g., Langfuse, LangSmith)

• Familiarity with
• agent interoperability protocols
• (A2A, MCP, or equivalent).
• Experience designing
• multi‑modal AI solutions
• .
• Proven ability to
• lead external or vendor teams
• .
• Strong
• executive communication skills
• with the ability to explain technical trade‑offs to non‑technical stakeholders.
• **Immediate Joiner - Required**