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About This Role
Houston, Texas 77001 Posted October 1st, 2026
Job Type: Full Time
Job Category: IT
Job Description
Role -.Net Developer with AI Experience Location – Houston, TX FTE & Contract both
Role Overview: We are seeking a hands-on .NET Microservices Lead to define, build, and govern large-scale enterprise platforms. The role requires deep expertise in C#, .NET, distributed systems, AWS /Azure, security, performance engineering, and production reliability. The architect must remain close to the code, lead critical implementations, guide multiple engineering teams, and translate business goals into secure, scalable, and operable solutions. Practical exposure to AI/LLM integration is preferred.
Core Responsibilities: Hands-on Coding and Engineering Leadership
- Own critical code paths and develop production-grade services, frameworks, reusable components, and reference implementations using C# and .NET 8.
- Conduct code reviews and technical audits covering readability, concurrency, error handling, testability, security, performance, and operational readiness.
- Establish coding standards, design patterns, automated quality gates, unit and integration test practices, and secure development controls.
- Mentor senior engineers and technical leads while resolving complex implementation challenges and design conflicts.
Architecture and System Design
- Lead high-level and low-level design for business-critical, high-throughput, low-latency, and globally scalable platforms.
- Define service boundaries using domain-driven design, bounded contexts, API contracts, data ownership, and evolutionary architecture principles.
- Produce architecture blueprints, sequence and deployment views, non-functional requirements, trade-off analyses, and architecture decision records.
- Lead design reviews across teams and ensure solutions meet scalability, availability, recoverability, maintainability, compliance, and cost objectives.
- Drive modernization of monolithic and legacy platforms into cloud-native, independently deployable services.
Distributed Systems, Reliability and Operations
- Design resilient synchronous and asynchronous workflows using REST, messaging, event streaming, queues, and publish-subscribe models.
- Apply distributed systems patterns including Saga, CQRS, event sourcing where appropriate, idempotency, outbox, circuit breaker, bulkhead, timeout, retry with backoff, rate limiting, and distributed caching.
- Address consistency, availability, partition tolerance, ordering, duplicate delivery, concurrency, distributed transactions, and failure recovery through explicit design decisions.
- Define service-level indicators and objectives, health checks, graceful degradation, disaster recovery, multi-AZ or multi-region strategies, and capacity plans.
Production Troubleshooting and Observability
- Lead diagnosis and resolution of complex production incidents spanning application code, CLR, databases, messaging, containers, Kubernetes, networks, and AWS services.
- Perform root cause analysis using logs, metrics, traces, thread dumps, heap dumps, profiling, query plans, and distributed correlation IDs; convert findings into preventive actions.
- Define observability standards with OpenTelemetry, CloudWatch, Prometheus, Grafana, and centralized logging, including actionable alerts and operational dashboards.
- Improve incident response through runbooks, failure simulations, post-incident reviews, automated remediation, and measurable reliability improvements.
Performance and Scalability Engineering
- Engineer systems to achieve explicit latency, throughput, concurrency, availability, and resource-efficiency targets.
- Lead load, stress, endurance, spike, and scalability testing; analyze bottlenecks across code, CLR, database, cache, network, container, and cloud layers.
- Optimize .NET memory management, garbage collection, thread pools, connection pools, serialization, API payloads, SQL queries, indexing, partitioning, and caching.
- Drive evidence-based capacity planning and performance benchmarking before production releases.
Security Architecture
- Embed security by design through threat modeling, secure coding, least privilege, zero-trust principles, vulnerability management, and secure SDLC controls.
- Design authentication and authorization using OAuth 2.0, OpenID Connect, JWT, RBAC/ABAC, IAM roles and policies, and service-to-service identity.
- Protect APIs and data using encryption in transit and at rest, secrets management, key rotation, input validation, dependency scanning, audit logging, and abuse prevention.
- Partner with security and compliance teams to address privacy, regulatory, penetration-test, and remediation requirements.
Cloud, DevOps and Platform Architecture
- Architect AWS solutions using ECS/EKS, Lambda, API Gateway, SQS, SNS, EventBridge, CloudWatch, IAM, DynamoDB, RDS, ElastiCache, VPC, Route 53, and Secrets Manager.
- Define container, Kubernetes, CI/CD, infrastructure-as-code, release, rollback, environment, and deployment strategies using automated controls.
- Balance resilience, security, maintainability, operational simplicity, and cloud cost when selecting services and patterns.
Required Technical Expertise: Area
Required Expertise
C# and .NET
Expert C# 12 and .NET 8, ASP.NET Core, .NET, multithreading, concurrency, CLR internals, design patterns, clean architecture, enterprise integration patterns, and automated testing.
Microservices
Service decomposition, DDD, API design, event-driven architecture, resiliency, distributed data, service discovery, configuration, and backward-compatible evolution.
Messaging
Kafka, SQS, SNS, EventBridge or RabbitMQ; delivery semantics, schema evolution, ordering, replay, dead-letter handling, and idempotent consumers.
Data
PostgreSQL/MySQL/Oracle plus DynamoDB or MongoDB; modeling, query tuning, indexing, partitioning, transactions, replication, caching, and data lifecycle.
Cloud and DevOps
AWS architecture, Docker, Kubernetes, CI/CD, Terraform or CloudFormation, observability, automated testing, deployment strategies, and SRE practices.
Security
Application, API and cloud security; OAuth2/OIDC/JWT, IAM, encryption, secrets, threat modeling, vulnerability remediation, and secure SDLC.
AI and Engineering Innovation:
- Evaluate and integrate AI/LLM capabilities using AWS Bedrock, OpenAI APIs, RAG patterns, embeddings, vector databases, and appropriate guardrails.
- Guide secure and responsible AI integration, including data protection, prompt and output controls, observability, evaluation, cost, latency, and fallback behavior.
- Use AI-assisted engineering for code understanding, test generation, documentation, troubleshooting, and productivity improvements without weakening engineering controls.
Required Experience:Experience Area
Minimum / Expected Experience
Total software engineering
12-18 years
C# and .NET enterprise development
10+ years
Microservices and distributed systems
7+ years
Architecture and system design
5+ years
AWS or equivalent cloud architecture
5+ years
Technical leadership and governance
5+ years
Production troubleshooting, performance and security
5+ years
Preferred Qualifications and Competencies:
- AWS Solutions Architect Professional, AWS Security Specialty, TOGAF, CKA/CKAD, or comparable advanced certification.
- Experience with high-volume platforms, complex modernization, multi-team architecture governance, and AI-powered enterprise solutions.
- Exceptional architecture judgment, hands-on coding depth, analytical troubleshooting, performance mindset, security ownership, communication, and stakeholder influence.
Required Skills
Cloud Developer
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 8,943 AI roles we're tracking, AI/ML Engineer positions make up 75% of the market. At Realign, 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 $212,000 based on 11,866 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200.
Across all AI roles, the market median is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,250; VP: $235,000.
Realign AI Hiring
Realign has 21 open AI roles right now. They're hiring across AI/ML Engineer, AI Architect, Data Engineer, Data Scientist. Positions span Seattle, WA, US, TX, US, MN, US.
Location Context
Across all AI roles, 6% (521 positions) offer remote work, while 8,351 require on-site attendance. Top AI hiring metros: New York (2,878 roles, $221,000 median); San Francisco (2,416 roles, $263,811 median); Los Angeles (1,340 roles, $210,000 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 8,943 open positions tracked in our dataset. By seniority: 175 entry-level, 4,132 mid-level, 3,268 senior, and 1,368 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (521 positions). The remaining 8,351 roles require on-site or hybrid attendance.
The market median for AI roles is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 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 8,943 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,739), AI Product Manager (403), AI Software Engineer (402). 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 (175) are outnumbered by mid-level (4,132) and senior (3,268) 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 1,368 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (521 positions), with 8,351 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 $213,380. Top-quartile roles start at $264,148, and the 90th percentile reaches $319,310. 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 $253,100 median, while Prompt Engineer roles sit at $141,440. 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 (1,422 postings), Aws (808 postings), Azure (638 postings), Rag (555 postings), Gcp (457 postings), Pytorch (433 postings), Claude (341 postings), Kubernetes (337 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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