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About This Role
Job ID: 110887
Atlanta
Boston
Chicago
Dallas
Washington DC
Do you want to do work that matters, alongside supportive leaders who will help you grow faster than you ever thought possible? Are you a creative problem\-solver who is energized by challenges? You’ve come to the right place.
YOUR IMPACT
You will own extraction and evaluation architecture, design and evolve the multi\-stage AI pipeline that converts unstructured contracts and invoices into structured compliance findings —covering document ingestion, vision LLM extraction, clause parsing, normalization, and deterministic evaluation logic.
You are the deepest individual technical contributor on the Contract AI platform. You own the hardest engineering problems — extraction model architecture, compliance evaluation logic, pipeline performance at scale, and the agentic reasoning layer. You are not primarily a manager, but you raise the ceiling of what the team can build: through architecture, code, code review, and mentoring.
You'll lead GenAI and agentic engineering, build, tune, and productionize agentic workflows for contract analysis using LangGraph, Semantic Kernel, or equivalent frameworks. You will advance the platform's reasoning capabilities beyond single\-shot prompting into multi\-step, tool\-using AI agents.
You will set the technical bar for the team: own code quality, architecture reviews, and technical standards. You will be the go\-to resource for the hardest debugging, design, and performance challenges.
You'll also mentor Senior Software Engineers, drive accuracy at scale, own evaluation accuracy metrics across compliance case categories; design validation harnesses, prompt refinement workflows, and confidence scoring frameworks.
You will contribute across the stack, write Python and C\# in production; contribute to the React/TypeScript analytics dashboard when needed; and own end\-to\-end delivery of major platform features from design through deployment.
You'll accelerate client deployments, build reusable extractor and evaluator templates that reduce Proof\-of\-Value configuration time; document architectural patterns for delivery engineers to apply consistently across client environments. You'll stay at the frontier: continuously evaluate emerging agentic frameworks, multi\-modal models, and document\-understanding approaches; propose and prototype high\-value capabilities before they appear in competitors' roadmaps.
YOUR GROWTH
Driving lasting impact and building long\-term capabilities with our clients is not easy work. You are the kind of person who thrives in a high performance/high reward culture \- doing hard things, picking yourself up when you stumble, and having the resilience to try another way forward.
In return for your drive, determination, and curiosity, we'll provide the resources, mentorship, and opportunities you need to become a stronger leader faster than you ever thought possible. Your colleagues—at all levels—will invest deeply in your development, just as much as they invest in delivering exceptional results for clients. Every day, you'll receive apprenticeship, coaching, and exposure that will accelerate your growth in ways you won’t find anywhere else.
When you join us, you will have:
- Continuous learning: Our learning and apprenticeship culture, backed by structured programs, is all about helping you grow while creating an environment where feedback is clear, actionable, and focused on your development. The real magic happens when you take the input from others to heart and embrace the fast\-paced learning experience, owning your journey.
- A voice that matters: From day one, we value your ideas and contributions. You’ll make a tangible impact by offering innovative ideas and practical solutions, all while upholding our unwavering commitment to ethics and integrity. We not only encourage diverse perspectives, but they are critical in driving us toward the best possible outcomes.
- Global community: With colleagues across 65\+ countries and over 100 different nationalities, our firm’s diversity fuels creativity and helps us come up with the best solutions for our clients. Plus, you’ll have the opportunity to learn from exceptional colleagues with diverse backgrounds and experiences.
- World\-class benefits: On top of a competitive salary (based on your location, experience, and skills), we provide a comprehensive benefits package to enable holistic well\-being for you and your family.
YOUR QUALIFICATIONS AND SKILLS
8\+ years of software engineering experience, with a strong portfolio of production systems you architected and shipped
Expert\-level Python—application development, async patterns, type\-checking, testing frameworks (pytest), performance profiling
Production experience with Generative AI: Azure OpenAI, LangChain, Semantic Kernel, RAG architectures, embeddings, structured output extraction, evaluation harnesses
Strong C\#/.NET—OOP design, .NET Core / .NET 8, REST APIs, background services is required
Hands\-on experience with agentic AI frameworks in shipped production systems —LangGraph, AutoGen, CrewAI, or equivalents is required
Azure platform depth: AKS, Azure Functions, CosmosDB, Postgres, Azure OpenAI, Azure DevOps, Terraform. Distributed system design: microservices, event\-driven architecture, API design, multi\-tenant data isolation, observability
Enterprise engineering discipline: TDD, CI/CD, RBAC/IAM, secure coding, SQL performance tuning
TypeScript/React production experience for full\-stack coverage
Document understanding: vision LLMs (GPT\-4o Vision), OCR, multi\-modal extraction pipelines from PDF/DOCX
Familiarity with contract management, procurement, or invoice processing domains
Experience contributing to or consuming open\-source AI frameworks
Ability to communicate architecture decisions in writing and in technical reviews; comfort with client\-facing technical interaction
Please review the additional requirements regarding essential job functions of McKinsey colleagues.
Our unwavering commitment to integrity drives everything we do, guiding us to always act in the best interests of our clients, our people, and the communities we serve.
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 McKinsey & Company, 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. Senior-level AI roles across all categories have a median of $227,400.
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.
McKinsey & Company AI Hiring
McKinsey & Company has 5 open AI roles right now. They're hiring across Data Engineer, AI Software Engineer, Data Scientist. Positions span Atlanta, GA, US, Boston, MA, US.
Location Context
Across all AI roles, 15% (635 positions) offer remote work, while 3,657 require on-site attendance. Top AI hiring metros: New York (1,650 roles, $220,000 median); San Francisco (1,335 roles, $265,000 median); Los Angeles (708 roles, $214,112 median).
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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