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About This Role
##### Project description
Overview
The GenAI Solutions Architect is the hands\-on consulting architect who ensures AI use cases across the bank are designed and connected the right way: aligned to the enterprise AI platform architecture, the approved integration patterns, and the bank's governance standards. As business and technology teams stand up AI\-enabled applications, this role is their design partner, translating platform capabilities and standards into concrete, buildable solution architectures and reviewing designs before they harden.
This is a deeply technical role for an architect who still builds: producing reference architectures, integration patterns, and working examples, and pairing with delivery teams to accelerate adoption while preventing rework and governance escapes.
##### Responsibilities
Solution Architecture \& Design Consulting
Serve as the consulting architect for AI use\-case teams across the bank: shape solution designs, data flows, model access patterns, and integration approaches aligned to the enterprise AI platform and gateway architecture.
Produce and maintain reference architectures, design patterns, and working examples for common use\-case shapes (RAG applications, document processing, workflow automation, agent\-based patterns).
Review solution designs against platform standards and governance requirements before build; document findings and drive remediation with delivery teams.
Advise on model selection, prompt/context architecture, retrieval design, and oversight/guardrail patterns appropriate to each use case's risk tier.
Platform Alignment \& Standards Adoption
Ensure all designs route model access through the governed enterprise gateway with correct entitlements, quotas, and logging; prevent parallel or ungoverned access paths.
Translate governance standards into architecture requirements delivery teams can implement, and feed practical gaps back into the standards process.
Partner with platform engineering on the evolution of platform capabilities based on real use\-case demand.
Document network, identity, data\-classification, and environment\-separation considerations for solution designs.
Partner with Cybersecurity architecture on AI threat modeling, prompt\-injection risk, and adversarial\-testing requirements for solution designs.
Enablement \& Capability Transfer
Pair with application teams that lack AI delivery experience; provide hands\-on design and build guidance through their first implementations.
Create and deliver architecture enablement materials, design guides, and pattern documentation in the bank's repositories.
Support solution reviews in governance forums with clear, evidence\-based architecture assessments.
Transfer patterns, documentation, and working knowledge to bank FTEs throughout the engagement so capability persists post\-contract.
##### Skills
Must have
Minimum of 7 years' experience in solution architecture, application architecture, or senior engineering roles, including hands\-on delivery of cloud\-native applications.
Hands\-on experience architecting and delivering GenAI/LLM\-based solutions: model integration, RAG pipelines, prompt/context engineering, and agent or workflow patterns.
Strong Azure experience: Azure OpenAI/AI services, API Management, Entra ID, Key Vault, networking and landing\-zone concepts, and environment separation.
Demonstrated experience producing reference architectures, integration patterns, and design documentation adopted by multiple teams.
Experience designing within security, risk, and compliance constraints in a regulated environment.
Strong consulting skills: stakeholder communication, design facilitation, and the ability to influence without authority.
Nice to have
Preferred Qualifications
Financial services experience and familiarity with banking SDLC governance (design gates, architecture review, permit\-to\-build/operate models).
Experience with AI gateway/governance patterns: model allowlisting, entitlement\-based access, usage controls, audit logging.
Experience with vector stores, retrieval services, evaluation harnesses, and MCP/tool\-integration patterns.
Experience mentoring teams new to AI delivery.
##### Other
Languages
English: C1 Advanced
Seniority
Senior
Remote United States, United States of America
Req. VR\-124245
Analytics Solution Architecture
BCM Industry
29/07/2026
Req. VR\-124245
Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Luxoft, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $214,900 based on 6,420 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $194,400.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
Luxoft AI Hiring
Luxoft has 6 open AI roles right now. They're hiring across AI/ML Engineer, AI Software Engineer. Based in Remote, US.
Remote Work Context
Remote AI roles pay a median of $180,000 across 1,196 positions. About 15% of all AI roles offer remote work.
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (138) are outnumbered by mid-level (2,071) and senior (1,655) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $287,500 median, while Prompt Engineer roles sit at $145,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
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