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About This Role
### Job Information
Job Opening ID
OTSI\_2558\_JOBDate Opened
06/05/2026Industry
IT ServicesJob Type
Full timeCity
SeattleState/Province
WashingtonCountry
United StatesZip/Postal Code
00000### About Us
OTSI is a global technology partner providing enterprise IT consulting, digital solutions, and managed services. We help organizations modernize complex technology landscapes, harness the power of data, and build scalable AI\-led ecosystems to accelerate innovation and business growth. With over 26 years of experience, we consistently turn complex challenges into success stories through our strong technical capabilities and deep industry knowledge. Our global team of 1,800\+ professionals, spread across 6 countries, delivers cutting\-edge solutions for customers across Banking, Financial Services, Insurance, Transportation \& Logistics, Energy \& Utilities, Healthcare \& Life Sciences, Government, Hi\-Tech, Telecom \& Media, Manufacturing, and more.
Our focused technology areas:
- AI/ML (Agentic AI Solutions, GenAI/LLMs, Natural Language Processing, Intelligent Processing, Physical Intelligence)
- Data \& Analytics (Data Architecture, Data Engineering, Data Migration, Data Modernization, Big Data, Analytics and BI)
- Cloud (Cloud Computing, Cloud Migration, Cloud\-Native Architecture, Hybrid and Multi\-Cloud)
- Digital Engineering (Application Modernization, Product Engineering, Platform Engineering, DevSecOps)
- Quality Engineering (Manual Testing, Non\-functional testing, Test Automation, Digital Testing)
- Enterprise Platforms (SAP, Microsoft, Oracle, etc.)
Our focused industries and target segments:
- Banking, Financial Services, \& Insurance
- Manufacturing, Transportation, \& Logistics
- Energy \& Utilities
- Telecom \& Media
- Healthcare \& Life\-sciences
- Government \& Public Sector (FED, SLED, \& Partners)
- Hi\-Tech
### Job Description
OTSI (Object Technology Solutions, Inc) has an immediate opening for an AI \& Data Solutions Architect
Location: Seattle (Remote, some travel required)
We are seeking a highly technical, client\-facing AI \& Data Solutions Architect to lead enterprise engagements, drive presales strategy, and facilitate architecture design sessions. You will serve as a trusted advisor to our clients, architecture complex data modernization and AI adoption strategies. While this role heavily emphasizes client interaction, executive presentation, and architectural design, it requires a strong technical practitioner who is fully capable of engaging in hands\-on development and technical problem\-solving to support global delivery teams and ensure project success.
Key Responsibilities
- Strategic Presales \& Solution Architecture: Act as the lead technical strategist during sales cycles. Partner with Sales to shape deal strategy, facilitate architecture design sessions with C\-suite stakeholders, define solution scope, and build compelling business and technical narratives.
- End\-to\-End Architecture Design: Architect scalable, cloud\-native software solutions and modern data platforms (e.g. Microsoft Fabric, Databricks, Snowflake) aligned with enterprise analytics and AI initiatives.
- Delivery Oversight \& Hands\-On Execution: Provide technical leadership to global development and data engineering teams. Serve as the definitive technical escalation point who can configure systems, develop scripts, or build proofs\-of\-concept to ensure the delivery of critical project milestones.
- Advanced AI Strategy: Design robust AI/ML solutions that advance beyond foundational LLM integrations. Guide clients in implementing Agentic AI workflows, autonomous orchestration, and secure enterprise integrations utilizing frameworks such as the Model Context Protocol (MCP).
- Governance \& Optimization: Ensure architectural consistency, quality, and strict adherence to enterprise AI governance and security frameworks throughout the SDLC. Optimize cloud architectures across Azure, AWS, and GCP to balance innovation, performance, and cost efficiency.
- Research \& Development: Stay up to date with AI/ML technologies, advancements, and trends. Provide insights to guide internal R\&D efforts on company products, tools, and accelerators outside of client engagements.
Required Skills: Consulting, Presales \& Leadership
- Client Engagement: 8\+ years in client\-facing presales, consulting, or solution architecture roles. Proven ability to facilitate executive discussions, translate complex technical concepts into clear business value, and drive consensus among enterprise stakeholders.
- Executive Presentation: Exceptional white boarding and communication skills. Demonstrated capability to dynamically design and articulate modern data architectures for both engineering leadership and business executives.
- Global Collaboration: Experience mentoring development teams and partnering seamlessly across a global delivery model to ensure the successful hand off, translation, and execution of defined architectures.
Required Skills: Core Technical Expertise
- Cloud \& Data Platforms: 7\+ years designing cloud\-native architectures (Azure, AWS, or GCP). Deep architectural knowledge of modern data platforms (preferably Databricks or Microsoft Fabric) and distributed compute frameworks (Apache Spark).
- Applied AI \& Machine Learning: Strong architectural experience designing AI/ML solutions, vector databases, and RAG architectures. Expertise in developing Agentic AI systems and workflow automation utilizing frameworks such as LangChain and the Model Context Protocol (MCP).
- Practitioner Capability: Retained hands\-on engineering proficiency with a strong command of Python and SQL, alongside experience in highly scalable backend languages like Java or Go. Fully capable of executing detailed technical work and navigating the modern SDLC.
- AI Productivity \& Infrastructure: Active utilization of AI productivity tools (e.g., GitHub Copilot, Claude) to accelerate development. Solid understanding of containerization (Docker, Kubernetes) and CI/CD pipelines to ensure the reliable, scalable deployment of AI models into production environments.
- Enterprise Integration: Expertise in designing robust data pipelines, semantic models, and API integrations that seamlessly connect AI capabilities within complex, legacy enterprise environments (e.g., SAP, Oracle).
Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 3,824 AI roles we're tracking, AI/ML Engineer positions make up 71% of the market. At Object Technology Solutions Inc, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $178,940 based on 11,900 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $160,000.
Across all AI roles, the market median is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Safety ($274,200). By seniority level: Entry: $97,380; Mid: $160,000; Senior: $227,400; Director: $243,000; VP: $250,000.
Object Technology Solutions Inc AI Hiring
Object Technology Solutions Inc has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Seattle, WA, US.
Location Context
AI roles in Seattle pay a median of $228,000 across 1,009 tracked positions. That's 14% above the national median.
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 3,824 open positions tracked in our dataset. By seniority: 119 entry-level, 1,813 mid-level, 1,472 senior, and 420 leadership roles (Director, VP, C-Level). Remote roles make up 16% of the market (613 positions). The remaining 3,187 roles require on-site or hybrid attendance.
The market median for AI roles is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($293,500 median, 31 roles); AI Safety ($274,200 median, 51 roles); Research Engineer ($260,000 median, 401 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 3,824 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,702), Data Scientist (281), AI Software Engineer (258). 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 (119) are outnumbered by mid-level (1,813) and senior (1,472) 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 420 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 16% of all AI roles (613 positions), with 3,187 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 $200,000. Top-quartile roles start at $253,000, and the 90th percentile reaches $307,500. 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 $142,800. 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 (1,968 postings), Aws (1,203 postings), Azure (882 postings), Rag (877 postings), Gcp (735 postings), Prompt Engineering (587 postings), Pytorch (586 postings), Claude (554 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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