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About This Role
Overview:
Prodapt is the largest and fastest\-growing specialized player in the Connectedness industry, recognized by Gartner as a Large, Telecom\-Native, Regional IT Service Provider across North America, Europe and Latin America. With its singular focus on the domain, Prodapt has built deep expertise in the most transformative technologies that connect our world. Prodapt is a trusted partner for enterprises across all layers of the Connectedness vertical. Prodapt designs, configures, and operates solutions across their digital landscape, network infrastructure, and business operations – and craft experiences that delight their customers. Today, Prodapt’s clients connect 1\.1 billion people and 5\.4 billion devices, and are among the largest telecom, media, and internet firms in the world. Prodapt works with Google, Amazon, Verizon, Vodafone, Liberty Global, Liberty Latin America, Claro, Lumen, Windstream, Rogers, Telus, KPN, Virgin Media, British Telecom, Deutsche Telekom, Adtran, Samsung, and many more. A “Great Place To Work® Certified™” company, Prodapt employs over 6,000 technology and domain experts in 30\+ countries across North America, Latin America, Europe, Africa, and Asia. Prodapt is part of the 130\-year\-old business conglomerate The Jhaver Group, which employs over 30,000 people across 80\+ locations globally.
We are seeking a highly skilled Senior AI / Agentic AI Engineer to design, develop, and deploy enterprise\-grade AI and Agentic AI solutions in Irving, Texas. The ideal candidate will have deep expertise in Generative AI, Large Language Models (LLMs), Agentic AI frameworks, cloud\-native application development, and modern software engineering practices. You will play a key role in architecting scalable AI solutions, mentoring engineering teams, and driving AI innovation across enterprise applications.
Responsibilities:
- Design, develop, and deploy enterprise\-scale AI and Agentic AI applications.
- Architect and implement AI solutions using LLMs, RAG, Agentic RAG, prompt engineering, and context engineering techniques.
- Build intelligent multi\-agent systems using frameworks such as LangChain, LangGraph, CrewAI, AutoGen, and Model Context Protocol (MCP).
- Develop scalable backend services and REST APIs using Python, TypeScript, NestJS, and Node.js.
- Design and implement cloud\-native microservices leveraging AWS/Azure, Docker, Kubernetes, and CI/CD pipelines.
- Optimize AI application performance, reliability, scalability, observability, and security.
- Integrate vector databases and knowledge retrieval systems such as Pinecone, Weaviate, Supabase Vector, or similar platforms.
- Collaborate with product managers, architects, designers, and engineering teams to deliver innovative AI\-powered solutions.
- Mentor engineers, conduct design and code reviews, and promote engineering best practices.
- Stay current with emerging AI technologies and recommend innovative approaches to improve products and engineering processes.
Requirements:
### Experience
- 8\+ years of software engineering experience.
- Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Engineering, or a related field.
- Relevant AI, Cloud, or Kubernetes certifications are a plus.
- 3\+ years of hands\-on experience building production\-grade Generative AI or AI\-powered applications.
- 2\+ years of experience designing and implementing Agentic AI solutions.
- Experience leading technical design, architecture, and mentoring engineering teams.
### Technical Skills
- Strong programming expertise in Python, TypeScript, Node.js, and NestJS.
- Experience with Java or Go is an added advantage.
- Strong understanding of Generative AI, LLMs, prompt engineering, RAG, Agentic RAG, and AI evaluation techniques.
- Hands\-on experience with LangChain, LangGraph, CrewAI, AutoGen, MCP, and modern AI orchestration frameworks.
- Experience with vector databases such as Pinecone, Weaviate, Supabase Vector, ChromaDB, or similar technologies.
- Strong understanding of software architecture, distributed systems, REST APIs, and microservices.
- Experience with AWS and/or Azure, Docker, Kubernetes, Terraform/Ansible, and CI/CD pipelines.
- Familiarity with AI\-assisted development tools such as Claude Code, Cursor, Devin, Windsurf, or Replit.
- Strong knowledge of software testing, application monitoring, performance optimization, and observability.
### Preferred Skills
- Experience building high\-volume, enterprise\-scale applications.
- Knowledge of secure AI deployments, AI governance, and responsible AI practices.
- Strong analytical, problem\-solving, and communication skills.
- Ability to work independently while collaborating effectively across cross\-functional teams.
- Passion for emerging AI technologies and continuous learning.
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 Prodapt Solutions, 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.
Prodapt Solutions AI Hiring
Prodapt Solutions has 1 open AI role right now. They're hiring across AI Software Engineer. Based in Irving, TX, 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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