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About This Role
##### Project description
Client is building its enterprise GenAI capability across two closely partnered organizations: the GenAI Platform \& Enablement organization (governed AI gateway on Azure API Management, model access via Azure AI Foundry today with a deliberately venue\-agnostic design, model integrations, retrieval services, environments, observability) and the Developer Experience organization (SDLC automation and tooling, quality engineering and test data management, site reliability engineering). Because the engineering foundations overlap heavily, all Software Engineer III openings across both organizations are filled through this single posting: candidates are evaluated once against a common engineering bar, then routed to the track and team where their depth fits best.
Every seat is hands\-on. Engineers design, build, test, document, and operate; FTE seats additionally carry track ownership, contractor review, and production deployment under the bank’s controlled\-change model, while contractor seats (18 months, extension possible, contract\-to\-hire in intent) carry the front\-loaded build with capability transfer written in.
##### Responsibilities
Design, build, test, and operate production capabilities hands\-on, with documentation and evidence by default.
Leverage GenAI capabilities in your own engineering workflow — coding assistants, agent\-based automation, AI\-assisted testing and debugging — to accelerate delivery, including SDLC automation opportunities.
Work within the bank’s SDLC gates, security standards, and separation\-of\-duties model; produce the artifacts and evidence each gate requires.
Partner across governance, architecture, cybersecurity, and application teams; communicate clearly with technical and business audiences.
Pair for capability transfer — FTEs with contractor counterparts, contractors with FTE track owners — so knowledge stays when engagements end.
Participate in support rotations for live workloads with incident triage and root\-cause discipline.
##### Skills
Must have
SE III (61 IC): Associate’s \+ 7 years or Bachelor’s \+ 5 years systems analysis/application development experience (or combined 9 years). SE II (60 IC): Associate’s \+ 5 years or Bachelor’s \+ 3 years equivalent experience (or combined 7 years).
Strong software engineering fundamentals: architecture, API design, integration patterns, secure coding, testing, CI/CD, and operational support.
Hands\-on experience with GenAI models and AI\-assisted development workflows (coding assistants, prompt engineering, RAG/context engineering, or agent\-based automation).
Hands\-on Azure experience relevant to the target track (e.g., APIM, Entra ID, Key Vault, Azure AI services including Azure AI Foundry, Azure Monitor, IaC tooling) — current stack is Azure AI Foundry \+ APIM, with the platform designed to extend to additional venues and tools (e.g., AWS Bedrock, Google Vertex, direct provider APIs) without rework.
Experience delivering and operating production systems in a controlled\-change environment.
Strong communication, collaboration, documentation, and stakeholder engagement skills.
Nice to have
Demonstrated depth in one or more tracks above — candidates are routed by strongest track fit across both organizations.
Financial services or other highly regulated industry experience; familiarity with audit\-evidence expectations.
Experience with GitLab Duo, Azure AI Foundry, Copilot Studio, Dynatrace, Delphix, or comparable track\-relevant tooling.
Certifications or demonstrated training in cloud engineering, AI engineering, DevOps, SRE, or related disciplines.
##### Other
Languages
English: C1 Advanced
Seniority
Senior
Remote United States, United States of America
Req. VR\-124264
Information Governance (Platform)
BCM Industry
30/07/2026
Req. VR\-124264
Role Details
About This Role
AI Software Engineers build the applications and systems that AI models run inside. They own the API layers, data pipelines, frontend integrations, and infrastructure that turn a model into a product users interact with. Every AI company needs engineers who can build the software around the AI.
The challenge is building reliable systems around inherently unreliable components. Models are probabilistic. They'll give different answers to the same question. They hallucinate. They're slow. They're expensive. Your job is to build an application layer that handles all of this gracefully while delivering a product that users trust and enjoy.
Across the 4,317 AI roles we're tracking, AI Software Engineer positions make up 7% of the market. At Luxoft, this role fits into their broader AI and engineering organization.
AI Software Engineer roles are among the most numerous in the AI job market. Every company deploying AI needs software engineers who understand AI integration patterns. The demand is broad, spanning startups to enterprises, across every industry adopting AI capabilities.
What the Work Looks Like
A typical week includes: building API endpoints that serve model inference with caching and fallback logic, designing the data pipeline that feeds context to a RAG system, implementing streaming responses in the frontend, debugging a race condition in the async inference pipeline, and optimizing database queries for the vector search layer. It's full-stack engineering with AI at the center.
AI Software Engineer roles are among the most numerous in the AI job market. Every company deploying AI needs software engineers who understand AI integration patterns. The demand is broad, spanning startups to enterprises, across every industry adopting AI capabilities.
Skills Required
Full-stack engineering skills with AI integration experience. Python and TypeScript are the most common requirements. You'll need to understand API design, database architecture, and how to build reliable systems around probabilistic outputs. Experience with streaming, async processing, and caching patterns is increasingly important as real-time AI applications proliferate.
Knowledge of vector databases, embedding APIs, and LLM integration patterns (function calling, structured outputs, retry logic) differentiates AI software engineers from general software engineers. Understanding cost optimization (caching strategies, model routing, batched inference) is valuable since inference costs can dominate application economics.
Strong postings describe the product you'll be building, the AI integration patterns you'll work with, and the scale requirements. Look for companies that have existing AI features and need engineers to improve and expand them, not companies that are 'planning to add AI' someday.
Compensation Benchmarks
AI Software Engineer roles pay a median of $218,500 based on 729 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 Software Engineer roles include Software Engineer, Full-Stack Developer, Backend Engineer.
From here, career progression typically leads toward Staff Engineer, AI Architect, Engineering Manager.
If you're a software engineer, you're already 80% there. Learn the AI integration patterns: RAG, streaming inference, function calling, structured outputs. Build a project that demonstrates you can wrap an AI model in a production-quality application with proper error handling, caching, and user experience. That's the portfolio piece that gets you hired.
What to Expect in Interviews
Technical screens look like standard software engineering interviews with an AI twist. Expect system design questions about building reliable applications around probabilistic models: handling streaming responses, implementing retry logic for API failures, and designing caching strategies for LLM outputs. Coding rounds test standard algorithms plus practical integration patterns like async processing and rate limiting.
When evaluating opportunities: Strong postings describe the product you'll be building, the AI integration patterns you'll work with, and the scale requirements. Look for companies that have existing AI features and need engineers to improve and expand them, not companies that are 'planning to add AI' someday.
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).
AI Software Engineer roles are among the most numerous in the AI job market. Every company deploying AI needs software engineers who understand AI integration patterns. The demand is broad, spanning startups to enterprises, across every industry adopting AI capabilities.
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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