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About This Role
7\+ Years
Austin
Full\-Time
Job Title: GenAI Architect (Watsonx)Experience: 7\+ YearsLocation: Remote (USA) Role Overview:
We are seeking an experienced AI Solutions Architect / Machine Learning Engineer with 7–12 years of hands\-on expertise in building, deploying, and scaling AI/ML solutions across enterprise environments. The ideal candidate has deep knowledge of modern AI platforms—including Watsonx Gov, Watsonx Orchestrate, Google Vertex AI, and Azure Copilot—and can translate business requirements into secure, compliant, and production‑ready AI systems.
You will lead end‑to‑end ML pipeline development, architect cloud\-native and hybrid AI solutions, and guide cross\-functional teams in delivering responsible, scalable, and high‑impact AI capabilities. ResponsibilitiesAI \& ML Architecture* + Design and architect enterprise\-grade AI/ML solutions aligned with organizational goals.
+ Implement cloud\-native and hybrid architectures supporting scalable AI deployments.
+ Evaluate AI platforms and tools (Watsonx, Vertex AI, Azure Copilot, LLM ecosystems) and recommend optimal solutions.
Model Development \& Deployment* + Build and optimize ML models, including generative AI, NLP, predictive modeling, and automation workflows.
+ Develop end\-to\-end ML pipelines involving ingestion, preprocessing, training, validation, deployment, and monitoring.
+ Set up MLOps workflows for continuous integration, delivery, and model governance.
AI Platform Integration* + Integrate AI capabilities into existing enterprise systems through APIs, orchestration tools, and automation frameworks.
+ Leverage enterprise AI platforms (Watsonx, Vertex AI, Azure Copilot) for secure and compliant deployments.
+ Implement guardrails and governance to ensure responsible AI usage.
Cross\-Functional Collaboration* + Work closely with engineering, data, cloud, and security teams to deliver production\-ready AI solutions.
+ Partner with business stakeholders to identify AI opportunities and translate them into technical requirements.
+ Provide technical leadership, mentorship, and architectural guidance across AI initiatives.
Governance \& Compliance* + Ensure AI implementations meet regulatory, ethical, and security requirements.
+ Document architecture, standards, and best practices to support auditability and compliance.
Required Skills* + 6–12 years of experience in AI/ML engineering, solution architecture, or enterprise AI development.
+ Strong expertise in ML model development, generative AI, LLMs, automation, and MLOps.
+ Hands\-on experience with Watsonx Gov, Watsonx Orchestrate, Google Vertex AI, Azure Copilot, or similar platforms.
+ Deep understanding of AI governance, responsible AI frameworks, and compliance requirements.
+ Proficiency in Python and ML frameworks such as TensorFlow, PyTorch, Hugging Face, or Scikit\-learn.
+ Strong background in cloud computing (AWS/Azure/GCP) with experience deploying scalable AI workloads.
+ Experience developing and maintaining ML pipelines and production MLOps workflows.
+ Ability to translate business needs into scalable AI architectures and actionable technical plans.
+ Excellent communication and stakeholder management skills.
Good to Have* + Experience with RAG (Retrieval\-Augmented Generation), vector databases, and LLM fine‑tuning.
+ Certification in AI, cloud architecture, or ML engineering (AWS/Azure/GCP).
+ Background in cybersecurity, risk management, or AI policy frameworks.
+ Experience with containerization technologies (Docker, Kubernetes).
+ Knowledge of data engineering, ETL, and enterprise data platforms.
+ Familiarity with enterprise automation systems (UiPath, Power Automate).
Benefits: \-* Competitive salary and performance\-based bonuses.
- Comprehensive health insurance.
- Paid time off and flexible working hours.
- Professional development opportunities and support for continuing education.
- Udemy Learning Licenses to enhance skills and stay up\-to\-date with the latest technology.
P99soft is an equal\-opportunity employer
P99soft is an equal\-opportunity employer. At P99soft, we are committed to providing equal employment opportunities regardless of job history, disability, gender identity, religion, race, color, caste, marital/parental status, veteran status, or any other special status. We stand against the discrimination of employees and individuals and are proud to be an equitable workplace that welcomes individuals from all walks of life if they fit the designated roles and responsibilities. Life@P99soft
At P99soft, we believe in creating a work environment that balances professional excellence with personal well\-being. Our vibrant office culture is built on mutual respect, inclusivity, and a shared passion for innovation. We host regular team\-building activities, workshops, and social events to foster a sense of community and collaboration among our employees. Join us and be part of a team that values your contributions and supports your growth About Us
P99soft is a leading IT Services and IT Consulting company dedicated to making our customers' business operations seamless by providing them with digital services and applications using the latest technologies. We are committed to bringing excellence to all our creations. We need passion, empathy, and being customer\-focused to achieve technical excellence. We believe in transparency through open communication and work with honesty and integrity while dealing with our customers and partners. Our Vision: “To lead digital transformation for our customers, delighting them at every step, while empowering our employees and fostering success.”Our Mission: “To attain our objectives within a framework of integrity, transparency, and respect for our clients, employees, partners, and the community, fostering mutual trust and sustainable growth.”
Journey towards digital transformations begins here. Our talented teams help you make informed decisions, enhance operation efficiencies, create state\-of\-the\-art visual interfaces, and design dynamic workflows to bring a better customer experience. Our engineering teams take complete ownership of assigning the right talent and architecting solutions using the latest technologies.
Role Details
About This Role
This role sits at the intersection of AI and engineering, building systems that bring machine learning capabilities into production environments. The scope varies by company, but the common thread is applying AI technology to solve real business problems at scale. Most AI roles today require a combination of software engineering fundamentals and domain-specific ML knowledge, with the exact mix depending on the team's maturity and the product they're building.
The AI job market is evolving fast. New role categories emerge as companies figure out what they need to ship AI-powered products. What matters most is the ability to learn quickly, build working systems, and iterate based on real-world performance data. The specific title matters less than the skills you bring and the problems you can solve. Companies are past the experimentation phase and want engineers who can deliver production-quality systems that work reliably at scale.
Across the 26,159 AI roles we're tracking, AI Architect positions make up 1% of the market. At p99soft, this role fits into their broader AI and engineering organization.
AI hiring keeps growing across industries. Companies in tech, finance, healthcare, and retail are all building AI teams. The strongest demand is for people who can bridge the gap between AI research and production engineering. The shift toward generative AI has created new role types (LLM Engineer, Prompt Engineer, AI Agent Developer) that didn't exist three years ago, while traditional roles (Data Scientist, ML Engineer) have evolved to incorporate LLM capabilities.
What the Work Looks Like
Day-to-day work involves a mix of building, debugging, and collaborating. You'll write code, review pull requests, participate in design discussions, and work with cross-functional teams (product, design, data) to define what AI features should do and how they should behave. Expect to spend time on both technical implementation and communication. Most AI teams operate in two-week sprint cycles, with regular demos and retrospectives. The ratio of heads-down coding to meetings and reviews varies by seniority, with senior roles spending more time on architecture decisions and mentorship.
AI hiring keeps growing across industries. Companies in tech, finance, healthcare, and retail are all building AI teams. The strongest demand is for people who can bridge the gap between AI research and production engineering. The shift toward generative AI has created new role types (LLM Engineer, Prompt Engineer, AI Agent Developer) that didn't exist three years ago, while traditional roles (Data Scientist, ML Engineer) have evolved to incorporate LLM capabilities.
Skills Required
Python and cloud platform experience are common requirements. Specific skill needs vary by company and focus area, but familiarity with ML frameworks, data pipelines, and API design covers the basics for most roles. RAG (Retrieval-Augmented Generation), vector databases, and LLM API integration are increasingly standard requirements across role types.
Beyond the core stack, communication skills matter more than many technical candidates realize. The ability to explain AI capabilities and limitations to non-technical stakeholders is a differentiator at every level. Technical writing, documentation, and clear thinking about tradeoffs are underrated skills in AI roles. Experience with evaluation methodology (how to measure whether an AI system is working well) is becoming a core requirement, especially for roles that involve LLM integration.
Look for job postings that specify the problems you'll work on, the tech stack, and the team structure. Vague postings that list every AI buzzword are often a sign the company hasn't figured out what they need. Strong postings describe the product context, the team you'd join, and the specific challenges you'd tackle.
Compensation Benchmarks
AI Architect roles pay a median of $292,900 based on 108 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $131,300.
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 Safety ($274,200). By seniority level: Entry: $76,880; Mid: $131,300; Senior: $227,400; Director: $244,288; VP: $234,620.
p99soft AI Hiring
p99soft has 1 open AI role right now. They're hiring across AI Architect. Based in Austin, TX, 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 Architect roles include Software Engineer, Data Scientist, Data Analyst.
From here, career progression typically leads toward Senior Engineer, AI Architect, Engineering Manager, Principal Engineer.
Focus on building things that work. A deployed project that solves a real problem is worth more than any certification. Contribute to open-source, build portfolio projects, and invest in fundamentals (software engineering, statistics, systems design) rather than chasing the latest framework. The AI field moves fast, but the engineers who succeed long-term are the ones with strong fundamentals who can adapt to new tools and paradigms as they emerge.
What to Expect in Interviews
AI interviews typically combine coding challenges (Python-focused), system design questions tailored to the role, and discussions about your experience with relevant tools and frameworks. Strong candidates demonstrate both technical depth and the ability to make pragmatic engineering tradeoffs. Prepare portfolio projects that demonstrate end-to-end capability rather than isolated skills.
When evaluating opportunities: Look for job postings that specify the problems you'll work on, the tech stack, and the team structure. Vague postings that list every AI buzzword are often a sign the company hasn't figured out what they need. Strong postings describe the product context, the team you'd join, and the specific challenges you'd tackle.
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 hiring keeps growing across industries. Companies in tech, finance, healthcare, and retail are all building AI teams. The strongest demand is for people who can bridge the gap between AI research and production engineering. The shift toward generative AI has created new role types (LLM Engineer, Prompt Engineer, AI Agent Developer) that didn't exist three years ago, while traditional roles (Data Scientist, ML Engineer) have evolved to incorporate LLM 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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