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About This Role
AI Full Stack Architect
12 months contract
Remote (Ocassional Travel)
Tax Term: W2
About the Role
We are seeking an AI Full Stack Architect to join our AI Center of Excellence. This is a senior individual-contributor and technical leadership role for someone who has spent years in the trenches — building, shipping, and scaling AI-powered systems — and is now ready to set the architectural direction for the next generation of intelligent applications.
You will own the end-to-end design and delivery of agentic AI systems, LLM-powered platforms, and full stack applications that operate at enterprise scale. You bring deep hands-on expertise across AWS Bedrock, Python, Node.js, and modern front-end frameworks, and you know how to translate that into production-grade architecture that others can build on.
Level & Scope
DIMENSION
EXPECTATION
Seniority
Architect / Principal — senior IC with org-wide technical influence
Experience Bar
10+ years in software engineering; 4+ years in AI/ML engineering
Depth
Expert-level in at least two of: agent development, LLM integration, cloud-native backend
Breadth
Fluent across the full stack — infra, backend, AI layer, and front-end
Leadership
Drives architecture decisions, mentors engineers, sets standards
Ambiguity
Comfortable defining the problem, not just solving it
Impact
Platform-level — your decisions affect multiple teams and products
Key Responsibilities
Agent Architecture & Development
Architect and build autonomous, multi-step, and tool-using AI agents using AWS Bedrock Agents and leading agentic frameworks.
Design multi-agent orchestration topologies — including supervisor/worker patterns, parallel execution, and handoff protocols.
Establish agent design patterns: memory management, context windows, tool-call sequencing, retry logic, and failure recovery.
Define guardrails, safety layers, and responsible AI standards for all agent-based systems.
LLM / SLM Integration
Own the selection, integration, and lifecycle management of Large Language Models (Claude, GPT-4, Llama, Mistral) and Small Language Models (Phi-3, Gemma, Mistral 7B).
Design and implement RAG pipelines, prompt engineering standards, few-shot frameworks, and fine-tuning workflows.
Establish model benchmarking and evaluation criteria — balancing capability, latency, cost, and safety.
Cloud & Infrastructure (AWS)
Lead the design of AI infrastructure on AWS Bedrock — including foundation model access, Knowledge Bases, and Bedrock Agents.
Architect cloud-native backend services using Lambda, API Gateway, ECS/EKS, S3, and IAM.
Define infrastructure-as-code standards and CI/CD pipelines for AI workloads.
Full Stack Development
Build and architect full stack applications — React/Next.js front ends, Node.js backend services, and Python agent/ML pipelines.
Design streaming agent UIs, chat interfaces, and real-time AI-powered user experiences.
Own API design and integration patterns between front-end, backend, and AI layers.
Monitoring, Observability & Evaluation
Implement agent monitoring and evaluation pipelines using Fiddler AI and comparable platforms (LangSmith, Arize, Weights & Biases, Helicone).
Define and track agent performance metrics: accuracy, latency, hallucination rate, tool-call success rate, and cost-per-interaction.
Build feedback loops that drive continuous model and agent improvement.
Technical Leadership
Lead architecture reviews, design documents, and RFC processes for AI initiatives.
Mentor and upskill engineers on agent development, LLM integration, and AI best practices.
Partner with product, data, and platform teams to translate business requirements into scalable AI solutions.
Continuously evaluate emerging models, frameworks, and tooling — bringing the best to the team.
Required Qualifications
AWS & Cloud
AWS Bedrock — deep, hands-on experience building and deploying agents and models (Bedrock Agents, Knowledge Bases, foundation model APIs).
Strong working knowledge of the broader AWS ecosystem: Lambda, S3, IAM, API Gateway, ECS/EKS, CloudWatch.
Experience designing cloud-native, serverless, and containerized AI workloads
Salary Context
This $0K-$0K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $178K across 1623 roles with salary data).
View full AI/ML Engineer salary data →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 9,079 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At Tharu Technologies, 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 $211,950 based on 13,048 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($77) sits 100% below the category median. Disclosed range: $75 to $80.
Across all AI roles, the market median is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($256,000) and Research Engineer ($252,000). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,300; Director: $272,300; VP: $235,830.
Tharu Technologies AI Hiring
Tharu Technologies has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Atlanta, GA, US. Compensation range: $0K - $0K.
Location Context
Across all AI roles, 6% (531 positions) offer remote work, while 8,478 require on-site attendance. Top AI hiring metros: New York (3,078 roles, $221,000 median); San Francisco (2,646 roles, $261,000 median); Los Angeles (1,486 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 9,079 open positions tracked in our dataset. By seniority: 106 entry-level, 4,384 mid-level, 3,343 senior, and 1,246 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (531 positions). The remaining 8,478 roles require on-site or hybrid attendance.
The market median for AI roles is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($256,000 median, 66 roles); Research Engineer ($252,000 median, 457 roles); AI Engineering Manager ($251,622 median, 55 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 9,079 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,862), AI Product Manager (453), AI Software Engineer (451). 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 (106) are outnumbered by mid-level (4,384) and senior (3,343) 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 1,246 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (531 positions), with 8,478 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 $213,000. Top-quartile roles start at $263,911, and the 90th percentile reaches $318,400. 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 Safety roles lead at $256,000 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 (1,649 postings), Aws (898 postings), Azure (723 postings), Rag (671 postings), Gcp (536 postings), Pytorch (434 postings), Claude (410 postings), Kubernetes (406 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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