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About This Role
About ICA, Inc.
International Consulting Associates, Inc. is a rapidly growing company, located in the D.C./Metro area. We were founded in 2009 to assist government clients with evaluating and achieving their objectives. We have become a trusted advisor helping our clients by offering cutting\-edge innovation and solutions to complex projects. Our small company has grown significantly, and we're overjoyed at the opportunity to expand yet again!
We are results\-focused and have a proven track record supporting federal agencies and large government services primes in three main areas: Research and Data Analysis, Advanced\-Data Science, and Strategic Services. We currently support multiple analytics and research programs across HHS.
At ICA, we believe our success starts with our people. We foster a collaborative "one team" environment where work\-life balance isn't just talked about – it's prioritized. We're building dynamic, highly skilled teams in a welcoming and supportive atmosphere. If you're passionate about using your technical expertise to make a difference, we want to talk to you.
We are looking for a Principal AWS Data Platform \& ML Ops Architectto join our growing team!
ABOUT THE ROLE:
We are seeking a hands\-on Principal AWS Data Platform \& MLOps Architect to design and build a shared data and AI platform supporting multiple products and projects.
Your mandate will be to establish a unified AWS\-native platform, including shared data models, pipelines, services, and serving layers that can support document intelligence, semantic and multimodal search, RAG applications, data science projects, dashboards, and analytics. You will define the technical direction and build the platform capabilities through hands\-on development and by guiding other Data Engineers.
KEY RESPONSIBILITIES:
Architect and Build the Shared Platform
- Define the target AWS architecture for shared data, document\-processing, search, analytics, and AI capabilities.
- Design the canonical data model, ingestion and processing pipelines, storage patterns, APIs, event contracts, and serving layers.
- Build a reusable platform serving multiple products and projects with critical shared capabilities and establish repeatable implementation patterns for engineering teams.
- Develop an incremental migration and adoption strategy for bringing existing solutions onto the shared platform.
- Identify duplicated pipelines, services, infrastructure, and technical patterns across projects.
- Determine which capabilities should become shared platform services and which should remain project\-specific.
Define the Data and MLOps Architecture
- Design the AWS\-based path from data science deliverables to production by establishing architecture for model packaging, deployment, serving, registry, monitoring, drift detection, and retraining workflows.
- Define onboarding standards for new data products, models, search applications, and AI\-enabled services.
- Design platform capabilities supporting search indexes, embeddings, vector retrieval, RAG, and model evaluation.
Provide Technical Design Authority
- Lead architecture and design reviews before significant development begins.
- Establish reference architectures, approved patterns, architecture decision records, and engineering standards.
- Require teams to use shared platform capabilities where appropriate and evaluate justified exceptions.
- Ensure platform designs meet performance, scalability, reliability, security, auditability, disaster\-recovery, and cost requirements.
- Provide technical leadership and mentoring across data engineering, data science, software engineering, and platform teams.
- Partner with product and engineering leaders on a cross\-project platform roadmap.
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REQUIRED QUALIFICATIONS:
- Active AWS Certifications (Solutions Architect, Data Engineer or Machine Learning)
- Extensive experience designing and building enterprise data platforms or AI/ML platforms on AWS.
- Demonstrated experience creating shared platform capabilities adopted by multiple products, projects, or business units.
- Experience consolidating separate production data systems through incremental migration and adoption.
- Strong hands\-on AWS architecture experience key for Data, AI, ML Engineering including S3, DynamoDB, Lambda, ECS, Fargate, or AWS Batch, Step Functions, SQS, SNS, and EventBridge, OpenSearch Service, Glue and Athena, SageMaker, IAM, KMS, and CloudWatch
- Strong experience with data engineering, distributed systems, event\-driven architecture, APIs, data modeling, metadata, lineage, governance, and access control.
- Strong MLOps architecture experience, including model deployment, serving, registries, monitoring, drift detection, and retraining workflows.
- Experience designing search, vector retrieval, embedding, or RAG data foundations.
- Experience with infrastructure as code using AWS CDK, Terraform, or CloudFormation.
- Experience with multi\-account AWS environments, private networking, and AWS Well\-Architected principles.
- Ability and willingness to write production code and build initial critical platform components.
- Experience influencing senior engineers and enforcing architecture decisions across teams without direct management authority.
- Strong technical communication and decision\-making skills.
- Must be authorized to work in the United States and have lived in the US for 3 or more consecutive years.
- Must be able and willing to obtain a Public Trust Clearance
PREFERRED QUALIFICATIONS:
- Experience with document intelligence, OCR, multimodal search, data lake or lakehouse platforms, or generative AI systems.
- Experience with Amazon Textract, Amazon Bedrock, SageMaker, OpenSearch vector capabilities, or AWS Lake Formation.
- Experience working with regulated, sensitive, or access\-controlled data.
- Familiarity with audit, data provenance, evidence retention, tenant isolation, and model\-governance requirements.
- Experience establishing shared platform practices in consulting, professional\-services, or multi\-client environments.
- Experience with AWS Organizations, Control Tower, disaster recovery, and cloud cost optimization.
USE OF AI ASSITIVE TECHNOLOGY:
All application materials must be your own original work, and interviews must be completed independently. Use of AI\-generated content in applications or AI assistance during interviews will result in disqualification.
BENEFITS:
We invest in our team members so you can live your best life professionally and personally, offering a competitive salary and benefits.
- Health Insurance \-100% employer\-paid premiums – ICA covers the full cost of one of three offered medical plans
- Dental Insurance
- Vision insurance
- Health Spending Account
- Flexible Spending Account
- Life and Disability insurance
- 401(k) plan with company match
- Paid Time Off (Vacation, Sick Leave and Holidays)
- Education and Professional Development Assistance
- Remote work from anywhere within the continental United States
LOCATION \& TELEWORK
This is a remote position following Eastern Standard Time (EST). Candidates residing in the DMV area preferred.
ADDITIONAL INFORMATION:
ICA is an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender, gender identity or expression, national origin, genetics, disability status, protected veteran status, age, or any other characteristic protected by state, federal or local laws.
This policy applies to all terms and conditions of employment, including recruiting, hiring, placement, promotion, termination, layoff, recall, transfer, leaves of absence, compensation, and training.
Salary Context
This $170K-$174K range is above the median for MLOps Engineer roles in our dataset (median: $168K across 34 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 4,317 AI roles we're tracking, MLOps Engineer positions make up 1% of the market. At ICAAI, 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 $203,000 based on 85 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($172K) sits 15% below the category median. Disclosed range: $170K to $174K.
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.
ICAAI AI Hiring
ICAAI has 2 open AI roles right now. They're hiring across AI Product Manager, MLOps Engineer. Based in Arlington, VA, US. Compensation range: $172K - $174K.
Remote Work Context
Remote AI roles pay a median of $180,000 across 1,196 positions. About 15% 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 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).
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 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.
Frequently Asked Questions
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