Level / Location: GCB4/5/6 - Various - co-located w
ith the FDT Tech
S
it w
ithin the FDT Tech team and close to users (business teams, delivery pods, transformation squads) as the accountable AI Engineer for rapid prototyping and delivery of advanced AI solutions. Design, build and
iterate production-viable AI prototypes and thin-slice solutions spanning advanced modelling, GenAI/RAG/agentic workflows, evaluation harnesses and safety controls-turning real user needs into working AI capabil
ities quickly, safely and repeatably. This role bridges product intent, data science and engineering execution, accelerating time-to-value while ensuring AI solutions are secure, supportable, efficient and aligned to enterprise standards and platforms. .
Reporting & Stakeholders: Solid line into FDT Tech Lead. Strong day-to-day partnership w
ith Product Owner(s), business SMEs, data/ML colleagues and delivery pod leads on scope, prior
itisation and trade-offs. Close collaboration w
ith platform teams (e.g., AI platform / Data platform), arch
itecture, and operations/support teams to ensure production readiness. Engage w
ith tooling/platform owners to provide structured feedback and reusable patterns from field delivery.
Add
itional note on AI Engineer counterpart: NA
Scope & author
ity: Accountable for shaping, building and delivering advanced AI prototypes and end-to-end thin-slice AI solutions close to users, including model selection, GenAI solution design, evaluation, safety controls, and efficiency optimisation. Owns local technical decisions required to deliver outcomes at pace, w
ithin approved arch
itecture patterns, engineering standards, controls and governance frameworks. Author
ity includes recommending AI approaches and evaluation methods, implementing guardrails and policies-as-code patterns, and escalating material risks, dependencies or control gaps.
Role description & core accountabil
ities
This role exists because high-impact AI delivery often requires engineers embedded w
ith users to rapidly discover the right AI approach, prove value beyond simple baselines, and then harden and deliver AI capabil
ities into production pathways. The AI Engineer accelerates learning loops while maintaining engineering discipline, ensuring what's built can scale, be supported and be reused.
Advanced modelling and algor
ithm selection - Choose and implement more complex approaches when needed (deep learning, graph ML, NLP, GenAI, multimodal, optimisation, RL where appropriate). Build prototypes that demonstrate lift over simpler baselines and justify added complex
ity.
LLM/GenAI solution design (if in scope) - Define prompting strategy, tool/function calling, RAG design (chunking, embeddings, retrieval evaluation), context window management and guardrails. Manage hallucination risk through grounding, c
itations, fallback behaviours and robust evaluation harnesses.
Evaluation frameworks & AI qual
ity - Create robust offline and online evaluation (golden datasets, human-in-the-loop review, red teaming, safety testing). Define model confidence, uncertainty handling and error taxonomies to drive measurable qual
ity improvements.
Model efficiency & production readiness - Optimise for latency and cost (distillation, quantisation, caching, batching, model selection). Ensure reproducibil
ity and handover-ready artefacts (model cards, evaluation reports, reproducible pipelines).
AI safety, secur
ity, and controls (technical depth) - Address prompt injection and data exfiltration risks, privacy constraints, and secure use of embeddings/vector stores. Help define guardrails and policies-as-code patterns w
ith engineering and risk partners.
Deployment partnership - Package models, prompts and retrieval components w
ith MLOps/engineering; define batch vs real-time serving patterns where relevant. Specify mon
itoring (qual
ity, drift, safety signals, cost/latency) and operational thresholds; support production handover.
Reusable components - Build shared libraries, templates, evaluation harnesses and patterns that multiple squads can adopt. Prefer reuse over bespoke and contribute reference implementations back to commun
ities of practice.
Technical leadership - Coach the squad on best practices; align w
ith central AI standards; review arch
itecture choices and implementation qual
ity. Keep delivery moving w
ith crisp weekly milestones and transparent trade-offs.
Success Profile - cr
itical
ity for this role
Cr
iterion
Cr
itical
ity
Experience
Delivering AI solutions into production pathways w
ith reproducibil
ity and handover artefacts
Essential
Advanced modelling and algor
ithm selection w
ith measurable lift over baselines
Essential
Evaluation frameworks (offline/online), golden datasets, and qual
ity metrics
Essential
AI safety/secur
ity risk identification and technical m
itigations (prompt injection, data leakage, privacy)
Essential
Rapid prototyping and
iterative delivery w
ith users
Essential
Capabil
ity
Customer / Client Centric
ity
Essential
Engineering discipline & qual
ity assurance
Essential
Evaluation rigour and evidence-led decision-making
Essential
Judgment and decision-making
Essential
Influencing and stakeholder alignment
Essential
Championing innovation through rapid
iteration
Essential
Strategic technical thinking
Desirable
Inspirational Leadership
Desirable
Talent stewardship
Desirable
Personal Attributes
Stamina & Resilience
Essential
Collaboration & influence
Essential
Leadership Span
Essential
Calibration rationale: This is a hands-on, user-adjacent AI engineering role optimised for speed-to-value and end-to-end delivery of advanced AI solutions. Core capabil
ities around engineering discipline, customer centric
ity, evaluation rigour, integration delivery and pragmatic decision-making are Essential. Broader strategic leadership and talent stewardship are Desirable, as the role contributes to scaling patterns and mentoring but does not typically own enterprise-wide strategy.
Experience - must-have
Proven experience delivering AI/ML solutions into production pathways, including reproducible training/inference and clear handover artefacts.
Strong abil
ity to rapidly prototype,
iterate w
ith users, and then harden AI solutions towards production readiness.
Experience selecting and implementing advanced modelling approaches (e.g., deep learning/NLP/graph/GenAI) and demonstrating lift over baselines.
Experience designing robust evaluation frameworks (offline/online), including golden datasets, human-in-the-loop review and clear qual
ity metrics.
Working knowledge of AI safety and secur
ity risks (e.g., prompt injection, data leakage, privacy constraints) and how to implement practical technical m
itigations.
Experience collaborating w
ith product and business stakeholders to shape MVP scope, define acceptance cr
iteria, and manage trade-offs.
Comfortable operating in ambiguous environments w
ith shifting requirements, while maintaining delivery discipline and transparency. Experience - strongly preferred
Experience delivering w
ithin regulated/controlled environments (risk, aud
it, secur
ity, data handling) and navigating governance pragmatically.
Experience w
ith GenAI arch
itectures including retrieval evaluation, grounding/c
itation patterns, and guardrails/fallback design.
Experience optimising models for latency/cost (quantisation, distillation, caching, batching) and setting cost/performance budgets.
Experience contributing reusable accelerators (shared libraries, templates, evaluation harnesses) and coaching other engineers.
Experience working in Value Streams / product-led delivery, transformation pods, or embedded engineering models. Behavioural skills
User-obsessed builder - stays close to users, validates fast, and focuses on measurable outcomes over perfect paperwork.
Pragmatic engineering discipline - moves quickly w
ithout skipping fundamentals (secur
ity, testing, operabil
ity, controls).
End-to-end ownership mindset - takes accountabil
ity from discovery through build, release and handover; doesn't throw work "over the wall".
Evidence-led decision-making - insists on baselines, measurable lift, and f
it-for-purpose evaluation before scaling complex
ity.
Clear communicator under pressure - explains options, trade-offs and risks in plain language; keeps stakeholders aligned.
Collaborative and inclusive - works well across roles and backgrounds; welcomes different perspectives to get to better solutions faster.
Proactive risk management - identifies safety, secur
ity, dependency and control risks early; escalates w
ith options, not surprises. Derailers / red flags
Adds model complex
ity w
ithout proving measurable lift over simpler baselines.
Builds AI prototypes that cannot be industrialised, creating rework or hidden technical debt.
Ignores safety, secur
ity, controls or operational needs in the name of speed; pushes fragile AI into production pathways.
Treats evaluation as an afterthought; lacks clear qual
ity metrics, error taxonomy, or reproducible evidence.
Over-builds bespoke components where reusable platforms/patterns exist.
Fails to manage dependencies or communicate risks early, causing delivery churn and stakeholder surprises.
Focuses on delivery completion w
ithout ensuring supportabil
ity, mon
itoring and clear ownership. Year 1 outcomes (scale & embed)
Consistent delivery of user-validated advanced AI prototypes that convert into production-viable thin-slice solutions w
ith clear value realised.
Advanced AI arch
itecture delivered (e.g., RAG/agentic workflow) w
ith measurable lift vs baseline and clear acceptance cr
iteria met.
Robust evaluation harness and safety tests implemented (golden datasets, red teaming, human review loops), w
ith clear qual
ity and risk controls.
Performance/cost optimisation plan executed, demonstrating improved latency and/or reduced run cost while maintaining qual
ity.
Reusable patterns/components contributed back to the Value Stream/platform commun
ities, reducing duplication across teams.
Strong production readiness discipline established (reproducibil
ity, mon
itoring, runbooks, handover), w
ith reduced post-release incidents and rework.
Clear, structured feedback provided to platform/tooling teams, influencing roadmap improvements based on field delivery real
ities.
Recognised as a dependable embedded AI engineering partner who unblocks delivery and raises overall AI engineering matur
ity. Pracyva is one of THE FASTEST GROWING specialized Recru
itmentConsulting firm in UK and Europe… Pracyva Lim
ited has local presence across UK , Europe ( Ireland, Netherlands , Poland ,Germany ), USA, Middle East and India , serving Top
IT clients for large volumes ..We are currently hiring for our Reputed client
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