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About This Role
USA
About project
Provectus is an AWS Premier Consulting Partner and AI consultancy featured in Forrester's AI Technical Services Landscape, with 15\+ years of experience and 400\+ engineers. We build production AI for global enterprises in partnership with Anthropic, Cohere, and AWS.
We are looking for a Senior Software Engineer or Solutions Architect with deep Python and Generative AI experience. Someone who ships Python services, RAG systems, tools, and agents daily, with AI embedded across their entire workflow.
You might be an experienced SA ready to hit the ground running, or a Senior Engineer and Tech Lead ready to step into that role. Either way, you will own the technical vision, lead discovery, drive architecture decisions, and become the authority clients and teams rely on.
What You’ll Do:
- Write clean, production\-grade Python across AI integrations, backend services, and RESTful APIs.
- Implement and optimize RAG systems for production use cases.
- Design and build LLM\-based and agentic AI solutions that address real client business challenges.
- Own the technical direction of client engagements from discovery through delivery.
- Support presales: discovery calls, technical proposals, scoping, and client\-facing demos.
- Lead architecture reviews, produce technical design documents, and contribute to standards across the Python practice.
- Mentor engineers, lead code reviews, and share knowledge across the team.
- Build and maintain strong relationships with key client stakeholders as a trusted technical advisor.
What You’ll Bring:
- Mindset
+ Full\-stack mindset, comfortable across AI, backend development, and cloud infrastructure.
+ Already using AI tools in your daily workflow (Claude Code, Copilot, or similar).
+ Proactive and self\-directed; you own outcomes end\-to\-end and spot problems before they're handed to you.
+ B2\+ English, comfortable collaborating across distributed, multicultural teams.
Presales \& Client Engagement
+ Owns the client technical relationship; leading discovery, decomposing ambiguous requirements into technical components, presenting architecture, and pushing back on scope when it doesn't match timeline or budget.
+ Produces scoped, phased delivery plans with clear deliverables, dependencies, and risks.
+ Experience with cost estimation and cloud architecture cost optimization.
Python, AI \& Cloud
+ 7\+ years building and running production systems, not only demos and POCs.
+ Strong understanding of AI/ML concepts and experience integrating AI/ML components into solutions.
+ Strong Python proficiency: OOP, design patterns, clean architecture, and performance optimization.
+ Experience building RESTful APIs with FastAPI, Django REST, or Flask.
+ Experience making and defending architectural trade\-off decisions: microservices vs monolith, sync vs event\-driven, SQL vs NoSQL.
+ Strong testing practices: pytest, mocking, and integration tests for AI systems.
+ Experience with Docker and Kubernetes.
+ Hands\-on experience building production LLM\-based applications and agentic workflows.
+ Experience with LLM APIs (OpenAI, Anthropic, or AWS Bedrock).
+ Experience building and optimizing RAG systems.
+ Understanding of LLM evaluation techniques and quality assurance approaches.
+ Experience deploying and maintaining AI/ML models in production environments.
+ Hands\-on experience with AWS (SageMaker, Bedrock, Lambda, ECS, S3, SQS, ECR, or similar); GCP considered.
+ Experience with React/Vue.
+ AWS and Claude Code Certifications.
Nice to Have
+ Experience with Streamlit or Gradio for AI prototyping.
+ Modern Python tooling (ruff, uv, pyproject.toml, pyright).
+ CI/CD pipeline experience (GitHub Actions, GitLab CI).
+ Experience in an additional language (Go, Node.js, or Rust).
+ Front\-end experience.
What We Offer:
- + Opportunity to work with cutting\-edge AI and cloud solutions.
+ Internal training programs (Leadership, Public Speaking, and more) with full support for AWS and other professional certifications.
+ Career growth: a clear path toward SA or beyond — we actively develop our engineers.
+ Access to the latest AI tools and premium subscriptions.
+ Remote with flexible hours.
+ Unlimited Vacation policy.
+ Generous health, vision, and dental insurance.
+ 401(K) matching plan.
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 26,159 AI roles we're tracking, AI Software Engineer positions make up 2% of the market. At Provectus, 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 $235,100 based on 665 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 $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Architect ($292,900). By seniority level: Entry: $76,880; Mid: $131,300; Senior: $227,400; Director: $244,288; VP: $234,620.
Provectus AI Hiring
Provectus has 8 open AI roles right now. They're hiring across AI Product Manager, AI Software Engineer, AI/ML Engineer. Positions span IL, US, PA, US, Remote, US.
Location Context
AI roles in Austin pay a median of $212,800 across 317 tracked positions. That's 16% above the national 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 26,159 open positions tracked in our dataset. By seniority: 2,416 entry-level, 16,247 mid-level, 5,153 senior, and 2,343 leadership roles (Director, VP, C-Level). Remote roles make up 7% of the market (1,863 positions). The remaining 24,200 roles require on-site or hybrid attendance.
The market median for AI roles is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. Highest-paying categories: AI Engineering Manager ($293,500 median, 28 roles); AI Architect ($292,900 median, 108 roles); AI Safety ($274,200 median, 19 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 26,159 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (23,752), AI Software Engineer (598), AI Product Manager (594). 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 (2,416) are outnumbered by mid-level (16,247) and senior (5,153) 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 2,343 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 7% of all AI roles (1,863 positions), with 24,200 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 $184,000. Top-quartile roles start at $244,000, and the 90th percentile reaches $309,400. 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 Engineering Manager roles lead at $293,500 median, while Prompt Engineer roles sit at $122,200. 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: Rag (16,749 postings), Aws (8,932 postings), Rust (7,660 postings), Python (3,815 postings), Azure (2,678 postings), Gcp (2,247 postings), Prompt Engineering (1,469 postings), Openai (1,269 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.
Frequently Asked Questions
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