FDE AI Engineer

Job Title: FDE AI Engineer

About Us

Capco, a Wipro company, is a global technology and management consulting firm. Awarded with Consultancy of the year in the British Bank Award and has been ranked Top 100 Best Companies for Women in India 2022 by Avtar & Seramount. With our presence across 32 cities across globe, we support 100+ clients across banking, financial and Energy sectors. We are recognized for our deep transformation execution and delivery.

WHY JOIN CAPCO?

You will work on engaging projects with the largest international and local banks, insurance companies, payment service providers and other key players in the industry. The projects that will transform the financial services industry.

MAKE AN IMPACT

Innovative thinking, delivery excellence and thought leadership to help our clients transform their business. Together with our clients and industry partners, we deliver disruptive work that is changing energy and financial services.

#BEYOURSELFATWORK

Capco has a tolerant, open culture that values diversity, inclusivity, and creativity.

CAREER ADVANCEMENT

With no forced hierarchy at Capco, everyone has the opportunity to grow as we grow, taking their career into their own hands.

DIVERSITY & INCLUSION

We believe that diversity of people and perspective gives us a competitive advantage.

Job Description:

FDE AI Engineer
Sit within 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 capabilities 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.

Role description & core accountabilities
This role exists because high-impact AI delivery often requires engineers embedded with users to rapidly discover the right AI approach, prove value beyond simple baselines, and then harden and deliver AI capabilities 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 algorithm 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 complexity.
• 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, citations, fallback behaviours and robust evaluation harnesses.
• Evaluation frameworks & AI quality — 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 quality improvements.
• Model efficiency & production readiness — Optimise for latency and cost (distillation, quantisation, caching, batching, model selection). Ensure reproducibility and handover-ready artefacts (model cards, evaluation reports, reproducible pipelines).
• AI safety, security, 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 with engineering and risk partners.
• Deployment partnership — Package models, prompts and retrieval components with MLOps/engineering; define batch vs real-time serving patterns where relevant. Specify monitoring (quality, 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 communities of practice.
• Technical leadership — Coach the squad on best practices; align with central AI standards; review architecture choices and implementation quality. Keep delivery moving with crisp weekly milestones and transparent trade-offs.