Role summary AI Architect

The AI Expert leads end‑to‑end lifecycle for data, analytics, and AI opportunities — from qualification and discovery through solution shaping, proposal creation, and handover to delivery. This is a highly client‑facing, hands‑on role that blends deep technical knowledge of modern data/GenAI platforms with the ability to craft clear business value cases and solution narratives. The role partners closely with Sales, Solution Architects, and SMEs to build winning proposals and guide customers to measurable outcomes.

Key responsibilities

Qualify and lead AI opportunity discovery: gather business goals, success metrics, constraints, and data landscape.
Design technical solutions and value cases: translate business needs into solution architecture, ROI/impact estimates, and measurable KPIs.
Build proposals and solution narratives: produce technical sections, scope, effort estimates, risk assessments, and delivery roadmaps aligned to client outcomes.
Hands‑on prototyping and PoCs: develop end‑to‑end prototypes (data ingestion, model/agent development, evaluation, and demo) to validate assumptions and accelerate sales.
Implement and architect GenAI solutions: design agentic workflows, tool‑augmented reasoning, multi‑agent orchestration, and integrations with enterprise data/services.
Collaborate with cross‑functional teams: align with Specialized Sales, Solution Architects, Data Engineers, ML Engineers, and Legal/Compliance on scope, architecture, and delivery readiness.
Handover to delivery: produce artifacts (architecture diagrams, runbooks, acceptance criteria) and support smooth transition to delivery teams.
Communicate clearly to stakeholders: present technical concepts, tradeoffs, timelines, and outcomes to technical and non‑technical audiences.
Risk and governance: identify data, security, and ethical risks; propose mitigation and compliance controls.
Stay current: evaluate emerging GenAI tools/frameworks and recommend adoption patterns.

Required qualifications & skills

Bachelor's or Master’s degree in Computer Science, Engineering, Data Science, or related field.

5+ years of professional experience in AI/ML, data engineering, or analytics roles; demonstrable client‑facing experience. Strong hands‑on experience with GenAI stacks and frameworks (e.g., OpenAI/Anthropic models, vector DBs, retrieval augmentation).
Practical proficiency in Python for prototyping, scripting, and model integration.
Direct experience designing and building agentic AI systems using frameworks such as LangChain, LangGraph, CrewAI, or equivalent — including multi‑agent workflows and tool integrations.
Experience with data pipelines, ETL, feature engineering, model evaluation, and MLOps fundamentals.
Solid understanding of cloud platforms and services (AWS/GCP/Azure) for data storage, compute, and deployment.
Excellent verbal and written communication skills; proven ability to present to executives and translate technical detail into business outcomes.
Strong problem solving, estimation, and scoping skills; capable of producing clear proposals and technical artifacts.

Preferred / nice‑to‑have

Hands‑on experience with vector databases (Pinecone, Milvus, Weaviate), embeddings, and retrieval systems.
Familiarity with LLM fine‑tuning, RAG architectures, and prompt engineering best practices.
Experience with orchestration and automation tools (Kubernetes, Airflow, Terraform).
Prior experience in consulting, pre‑sales, or solution engineering roles.
Knowledge of data governance, privacy regulations, and AI ethics frameworks.

Success metrics

Number and quality of qualified AI opportunities advanced to proposal/PoC.
Win rate and time‑to‑proposal for assigned opportunities.
Client satisfaction with discovery and solution handoff.
Accuracy of effort estimates and readiness of delivery artifacts.
Demonstrable business impact (projected ROI, KPIs) in proposals and PoCs.

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