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About This Role
Date Posted:
2026\-05\-28Country:
United States of AmericaLocation:
US\-CT\-REMOTEPosition Role Type:
RemoteU.S. Citizen, U.S. Person, or Immigration Status Requirements:
U.S. citizenship is required, as only U.S. citizens are authorized to access information under this program/contract.Security Clearance Type:
None/Not RequiredSecurity Clearance Status:
Not Required
At RTX, the world largest aerospace and defense company, 185,000 great minds are united by purpose and inspired to make a difference solving the world’s most complex problems.
With our three market leading businesses, world\-class operations and investments in research and development, we offer capabilities and opportunity no one else can. Together, we push the boundaries of known science and find new ways to connect and protect our world.
Pratt \& Whitney is a world leader in the design, manufacture and service of aircraft engines and auxiliary power systems and has been revolutionizing modern flight for over 100 years.
Join us and help shape the future of aerospace and defense.
Our Pratt Whitney Global Digital Technology team has a new exciting remote opportunity for a highly experienced, highly motivated Associate Director, Solutions Architect for Data, Analytics \& AI/ML Platforms.
What You Will Do:
The Associate Director, Solutions Architect for Data, Analytics \& AI/ML Platforms is a senior technical leader responsible for defining the end‑to‑end architecture of enterprise data platforms, advanced analytics solutions, and AI/ML capabilities.
This role drives platform modernization across cloud, Lakehouse, and AI ecosystems.
This leader combines deep technical expertise, enterprise systems thinking, and strong communication skills to guide development teams, influence business strategy, and ensure seamless integration across platforms such as Databricks, Azure, AWS, SAP S/4, and emerging AI toolsets.
The role functions as a key bridge between business, product, architecture, and Digital Technology organizations.
Success requires thought leadership, architectural rigor, strong stakeholder engagement, and the ability to design scalable, secure, and high‑performing data solutions across the enterprise.
Key Responsibilities:
Enterprise Architecture \& Strategy:
- Define the long‑term architecture for data, analytics, and AI/ML platforms in alignment with enterprise digital strategy.
- Develop reference architectures, design patterns, and platform standards for cloud, Lakehouse, and AI ecosystems.
- Define and lead technology proof of concepts to ensure feasibility of architecture solutions.
- Provide strong thought leadership in pursuit of modern architecture principles and technology modernization.
- Evaluate emerging technologies and provide strategic recommendations (AI, knowledge graphs, real‑time streaming, ML Ops, etc.).
- Drive continuous technology transformation to minimize technical debt.
Solution \& Platform Architecture:
- Architect end‑to‑end solutions spanning ingestion, transformation, governance, analytics, and ML operationalization.
- Lead architectural design for platforms such as Databricks, Snowflake, Azure Data Services, SAP S/4 connectors, and enterprise knowledge graph/ontology platforms.
- Define patterns for real‑time streaming, data virtualization, metadata frameworks, and multi‑cloud integration.
- Ensure platform scalability, performance, redundancy, cost optimization, and security.
Governance, Security \& Compliance:
- Collaborate with Cybersecurity and Data Governance teams to enforce data privacy, lineage, cataloging, and access control standards.
- Ensure compliance with government regulations, export control, and enterprise security policies.
Leadership \& Stakeholder Management:
- Provide architectural leadership across cross‑functional engineering teams and strategic programs.
- Serve as a trusted advisor to business executives, product owners, and technical stakeholders.
- Mentor and guide data engineers, architects, and analytics practitioners across the organization.
- Lead architecture governance forums and design reviews for major programs.
Qualifications You Must Have:
- Bachelor’s degree in Computer Science, Engineering, Data Science or related technical field and 12\+ years of applicable work experience; OR an Advanced degree in Computer Science, Engineering, Data Science or related technical field and 10\+ years of applicable work experience.
- U.S. citizenship is required, as only U.S. citizens are authorized to access information under this program/contract.
- 10\+ years of experience in data engineering, data architecture, analytics, AI/ML, or related technical domains.
- 5\+ years architecting cloud\-native data platforms (Azure, AWS, or GCP).
- Deep experience with Databricks, Lakehouse architectures, and modern ELT/ETL tooling.
- Strong understanding of distributed systems, real‑time data streaming, data governance, and API/Integration architectures.
- Demonstrated ability to design and implement enterprise‑scale data architecture solutions.
- Strong stakeholder management, communication, and executive engagement skills.
Qualifications We Prefer:
- Experience with SAP S/4HANA integrations, manufacturing data, or aerospace \& defense environments.
- Hands‑on knowledge of AI/ML frameworks (MLflow, Feature Stores, RAG, vector databases).
- Experience with knowledge graph / ontology platforms (e.g., Kobai, Neo4j, Databricks Graph technologies).
- Experience leading teams in Agile delivery environments.
- Proven ability to drive modernization, cloud migration, or digital transformation initiatives.
- Proven ability to drive modernization, cloud migration, or digital transformation initiatives.
Learn More \& Apply Now:
What is my role type?
In addition to transforming the future of flight, we are also transforming how and where we work. We’ve introduced role types to help you understand how you will operate in our blended work environment.
This role is:
Remote: Employees who are working in Remote roles will work primarily offsite (from home).
Candidates will learn more about role type and current site status throughout the recruiting process. For onsite and hybrid roles, commuting to and from the assigned site is the employee’s personal responsibility.
- *This requisition is eligible for an employee referral award. ALL eligibility requirements must be met to receive the referral award.*
*As part of our commitment to maintaining a secure hiring process, candidates may be asked to attend select steps of the interview process in\-person at one of our office locations, regardless of whether the role is designated as on\-site, hybrid or remote.*
The salary range for this role is 157,200 USD \- 298,800 USD. The salary range provided is a good faith estimate representative of all experience levels. RTX considers several factors when extending an offer, including but not limited to, the role, function and associated responsibilities, a candidate’s work experience, location, education/training, and key skills.
Hired applicants may be eligible for benefits, including but not limited to, medical, dental, vision, life insurance, short\-term disability, long\-term disability, 401(k) match, flexible spending accounts, flexible work schedules, employee assistance program, Employee Scholar Program, parental leave, paid time off, and holidays. Specific benefits are dependent upon the specific business unit as well as whether or not the position is covered by a collective\-bargaining agreement.
Hired applicants may be eligible for annual short\-term and/or long\-term incentive compensation programs depending on the level of the position and whether or not it is covered by a collective\-bargaining agreement. Payments under these annual programs are not guaranteed and are dependent upon a variety of factors including, but not limited to, individual performance, business unit performance, and/or the company’s performance.
This role is a U.S.\-based role. If the successful candidate resides in a U.S. territory, the appropriate pay structure and benefits will apply.
RTX anticipates the application window closing approximately 40 days from the date the notice was posted. However, factors such as candidate flow and business necessity may require RTX to shorten or extend the application window.*RTX is an Equal Opportunity Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, age, disability or veteran status, or any other applicable state or federal protected class. RTX provides affirmative action in employment for qualified Individuals with a Disability and Protected Veterans in compliance with Section 503 of the Rehabilitation Act and the Vietnam Era Veterans’ Readjustment Assistance Act.*
Privacy Policy and Terms:
Salary Context
This $157K-$298K range is above the median for MLOps Engineer roles in our dataset (median: $209K across 26 roles with salary data).
View full MLOps Engineer salary data →Role Details
About This Role
MLOps Engineers build the infrastructure that keeps ML models running in production. They own CI/CD pipelines for model deployment, monitoring for data drift and model degradation, and the tooling that lets data scientists ship faster. If ML Engineers build the models, MLOps Engineers build the roads those models travel on.
The job is fundamentally about reliability and velocity. Data scientists want to iterate fast. Product teams want stable predictions. Your job is to make both happen simultaneously. That means building deployment pipelines that catch regressions before they hit production, monitoring systems that alert on data drift before it degrades model performance, and self-service tooling that lets data scientists deploy without filing a ticket.
Across the 3,824 AI roles we're tracking, MLOps Engineer positions make up 1% of the market. At Pratt & Whitney, this role fits into their broader AI and engineering organization.
MLOps demand tracks closely with production ML adoption. As more companies move models from notebooks to production, the need for MLOps grows. The role is well-established at large tech companies and growing fast at mid-stage startups that are hitting the 'our models work in notebooks but break in production' phase.
What the Work Looks Like
A typical week involves: debugging a model deployment that's serving stale predictions, building a new monitoring dashboard for a feature team, writing Terraform for GPU-enabled inference clusters, reviewing pull requests for the ML platform's CI/CD pipeline, and meeting with data scientists to understand their pain points. You're the bridge between ML and infrastructure.
MLOps demand tracks closely with production ML adoption. As more companies move models from notebooks to production, the need for MLOps grows. The role is well-established at large tech companies and growing fast at mid-stage startups that are hitting the 'our models work in notebooks but break in production' phase.
Skills Required
Kubernetes, Docker, and cloud infrastructure are baseline. Most roles want experience with ML-specific tooling: MLflow, Kubeflow, Weights & Biases, or similar. Strong DevOps fundamentals matter more than ML theory. You need to understand model serving (TorchServe, Triton, vLLM), monitoring (Prometheus, Grafana), and infrastructure-as-code (Terraform, Pulumi).
GPU infrastructure knowledge is increasingly valuable as LLM inference becomes a major cost center. Understanding GPU scheduling, multi-node training setups, and inference optimization (quantization, batching, caching) puts you in the top tier. Experience with model registries and feature stores rounds out the profile.
Good MLOps postings specify their ML stack, infrastructure scale, and the problems they're solving (deployment velocity, cost optimization, monitoring gaps). Red flag: companies that want MLOps but don't have any models in production yet. You'll end up doing general DevOps instead.
Compensation Benchmarks
MLOps Engineer roles pay a median of $217,200 based on 76 positions with disclosed compensation. Director-level AI roles across all categories have a median of $243,000. This role's midpoint ($228K) sits 5% above the category median. Disclosed range: $157K to $298K.
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.
Pratt & Whitney AI Hiring
Pratt & Whitney has 1 open AI role right now. They're hiring across MLOps Engineer. Based in Hartford, CT, US. Compensation range: $298K - $298K.
Remote Work Context
Remote AI roles pay a median of $169,035 across 1,817 positions. About 16% of all AI roles offer remote work.
Career Path
Common paths into MLOps Engineer roles include DevOps Engineer, Platform Engineer, Data Engineer.
From here, career progression typically leads toward ML Platform Lead, Infrastructure Architect, Engineering Manager.
DevOps engineers with ML curiosity have the shortest path. You already understand deployment, monitoring, and infrastructure. Add ML-specific knowledge (model serving, data pipelines, experiment tracking) and you're competitive. The career ceiling is high: ML Platform Lead roles at top companies pay well because the infrastructure complexity is enormous.
What to Expect in Interviews
Interviews emphasize infrastructure and reliability. Expect questions about CI/CD for ML models, monitoring for data drift, and how you'd design a model serving platform that handles 10K requests per second. Coding rounds focus on Python and infrastructure-as-code (Terraform, Helm). Be ready to discuss tradeoffs between different model serving frameworks and how you'd handle rollback when a new model degrades performance.
When evaluating opportunities: Good MLOps postings specify their ML stack, infrastructure scale, and the problems they're solving (deployment velocity, cost optimization, monitoring gaps). Red flag: companies that want MLOps but don't have any models in production yet. You'll end up doing general DevOps instead.
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).
MLOps demand tracks closely with production ML adoption. As more companies move models from notebooks to production, the need for MLOps grows. The role is well-established at large tech companies and growing fast at mid-stage startups that are hitting the 'our models work in notebooks but break in production' phase.
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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