Business value
- Faster delivery: Parallel work across data, modelling, and serving shortens the path from PoC to production.
- Higher quality: Clear ownership of data pipelines, evaluations, and monitoring raises reliability.
- Lower risk: Governance, security, and cost controls are designed in, not bolted on later.
Common impact areas
- Personalised search and recommendations, churn/risk prediction.
- Document understanding (OCR, extraction, summarisation), intelligent assistants.
- Computer vision for QA, defect detection, or logistics optimisation.
Team Composition & Responsibilities
Core roles
- Product & Delivery Lead: Turns business goals into measurable AI outcomes, prioritises scope.
- Data Engineer: Builds pipelines, quality checks, feature stores, and warehouse integrations.
- ML/DL Engineer: Trains/fine-tunes models (NLP/CV/LLMs), designs evals, iterates baselines → SOTA.
- MLOps Engineer: CI/CD for models, registries, canary deploys, monitoring, rollback plans.
- Full-stack/Platform Engineer: Serves models via APIs, optimises latency, integrates with app/backend.
Optional specialists
- Prompt/LLM Engineer, Analytics Engineer, Security & Compliance, UX for AI interfaces.
Hiring Process (Lean & High-Signal)
Step-by-step flow
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Define outcomes
- Example: “Reduce support resolution time by 20% via automated triage and suggested replies.”
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Sourcing & screening
- Prioritise portfolios with shipped systems and readable docs; check GitHub/tech blogs for real artefacts.
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Technical interviews
- Problem framing, data strategy, modelling choices, evaluation design, deployment and observability.
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Practical exercise (team)
- Small end-to-end task: dataset stub → baseline model → API endpoint → eval report (4–6 hours/person cap).
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Live review & architecture session
- Stress-test cost, latency, and failure modes; agree on guardrails and rollback.
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Reference checks
- Validate delivery cadence, communication, and production reliability.
Evaluation Checklists
Capability checklist
- Shipped ML to production with measurable KPI impact.
- Sensible baselines before complex architectures; ablations and error analysis.
- Clear MLOps story: versioning, registries, CI/CD, canaries, monitoring for drift/bias.
- Data quality practices: lineage, tests, PII handling, privacy-by-design.
Collaboration signals
- Writes decision docs and runbooks; explains trade-offs.
- Works iteratively with product/design; aligns metrics with business goals.
Delivery Blueprint & Best Practices
From PoC to production
- Week 0–2: Discovery, data audit, evaluation plan, baseline selection.
- Week 3–6: Training + initial serving, golden datasets, offline/online metrics.
- Week 7–10: Canary rollout, monitoring/alerts, cost-latency tuning, documentation.
Engineering standards
- Feature flags and kill-switches; reproducible training; infra as code.
- Optimisation toolkit: quantisation, distillation, batching, caching, vector search where relevant.
- Security & compliance: token hygiene, access controls, audit logs, retention policies.
Pricing & Engagement Models
Options to consider
- Dedicated team (in-house or partner): Long-term roadmap, deeper domain knowledge, predictable velocity.
- Augmented squad: Seed your team with niche expertise (e.g., MLOps, LLMs) for specific milestones.
- Fixed-scope pilot: Time-boxed engagement to de-risk use cases and validate ROI.
What to Prepare Before You Hire
- Clear problem statements and success metrics.
- Data access and governance decisions.
- Non-functional targets: latency, throughput, cost ceilings, availability SLOs.
- Stakeholder map and decision cadence (weekly demos, monthly reviews).
Conclusion
An effective AI team is cross-functional, outcome-driven, and operationally disciplined. Define crisp goals, hire for production experience and collaboration, and deliver in small, measurable increments that compound into durable competitive advantage.
Page Updated: 2025-10-06