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About This Role
If you are looking for a meaningful career where people work and act with passion, rethink the existing and always strive to find the best solution \- you have come to the right place. We develop future technologies to relentlessly make supply chains better.
We are a leader in supply chain software solutions, helping organizations streamline operations, reduce costs, and improve efficiency.
The Archer team at Infios is building the next generation of AI\-powered enterprise solutions at massive scale. We are looking for a Senior AI Platform Engineer with deep expertise in spec\-driven AI SDLC, strong hands\-on experience with AWS AI infrastructure (Bedrock, Bedrock Agents, Agent Core), and fluency across Java, TypeScript, and Python. You will champion a specification\-first approach to AI development — translating product requirements into rigorous AI specs, building LLM\-powered and agentic applications using Spring AI, and owning the full lifecycle from prototype through production on AWS. We value excellent problem solving, clear communication, and engineers who bring discipline and craft to AI product delivery.
What a day in the life looks like:
- Define AI feature specifications upfront — including acceptance criteria, evaluation metrics, prompt contracts, and expected behaviors — and champion this spec\-driven approach across the team.
- Own end\-to\-end AI feature delivery across the full AI SDLC: spec definition, prototyping, development, evaluation, deployment, and production monitoring.
- Build production\-grade LLM and agentic AI applications using Spring AI — including RAG pipelines, agent orchestration, tool\-use patterns, guardrails, and human\-in\-the\-loop workflows.
- Architect and operate AWS AI infrastructure (Bedrock, Bedrock Agents, Agent Core, SageMaker) alongside core AWS services (ECS/EKS, Lambda, S3, DynamoDB, RDS, API Gateway).
- Design and implement scalable microservices and distributed systems in Java, TypeScript, and Python that power the Archer AI platform.
- Build CI/CD pipelines for AI workloads — including LLM evaluation pipelines and automated regression testing for AI outputs — using Terraform, CloudFormation, Docker, Kubernetes, and GitHub Actions.
- Drive AI\-specific operational practices: observability, drift detection, quality scoring, feedback loops, and incident response for non\-deterministic systems.
- Communicate technical concepts clearly to both technical and non\-technical stakeholders; author AI specs, design documents, and architectural decision records.
- Mentor engineers, conduct thorough code reviews, and champion engineering excellence.
What you bring to the team:
- Spec\-Driven AI SDLC: Deep expertise in the AI software development lifecycle with a specification\-first mindset. Experience authoring AI feature specs (acceptance criteria, evaluation metrics, prompt contracts) and driving the full lifecycle from prototyping through evaluation frameworks, A/B testing, deployment of non\-deterministic systems, and production monitoring (drift detection, quality scoring, feedback loops). Track record of shipping AI\-powered features through multiple product cycles with engineering rigor.
- AWS AI Infrastructure: Strong hands\-on experience with Amazon Bedrock, Bedrock Agents, Agent Core, SageMaker, and Amazon Q. Solid knowledge of core AWS infrastructure including compute (ECS/EKS, Lambda), databases (RDS, DynamoDB, ElastiCache ), networking (VPC, ALB, CloudFront), and security (IAM, KMS, Secrets Manager). Experience architecting AI infrastructure pipelines with cost optimization and high availability.
- LLM Frameworks \& Agentic AI: Hands\-on experience building production applications with Spring AI. Solid understanding of LLM application patterns (prompt management, RAG, context orchestration, vector stores, evaluation) and agentic workflows (multi\-step agents, tool\-use orchestration, planning loops).
- Java, TypeScript \& Python: 5\+ years of professional software engineering with strong proficiency across all three languages — Java (Spring Boot, Spring Cloud), TypeScript (Node.js, modern frameworks), and Python (AI tooling, evaluation frameworks). Comfortable choosing the right language for each task.
- Enterprise \& Large\-Scale Systems: Experience designing and operating distributed systems at scale. Familiarity with event\-driven architectures, message brokers (Kafka, SQS/SNS), caching (Redis, ElastiCache ), and relational/NoSQL database design.
- DevOps \& Infrastructure: Proficiency in CI/CD pipelines, Infrastructure as Code (Terraform, CloudFormation), containerization (Docker, Kubernetes/EKS), and GitOps workflows.
- Problem Solving \& Communication: Excellent analytical skills and the ability to tackle complex, ambiguous challenges independently. Outstanding written and verbal communication — able to articulate technical concepts to diverse audiences and collaborate effectively across teams.
- Education: Bachelor’s or Master’s degree in Computer Science , Software Engineering, or a related field (or equivalent practical experience).
Why join us
- Contribute to a solution recognized in the Gartner® Magic Quadrant™, delivering innovation and measurable value to global customers.
- Be part of a fast\-paced, global environment with international clients and colleagues.
- Access to competitive Medical, Dental, and Vision insurance.
- 401K matching program.
- Flexible Time Off, 11 paid holidays, and 1 volunteer day per year.
- A fun, collaborative, and innovative culture where your input matters.
Salary range : From $170K to 190K USD (final compensation based on experience, performance, and internal equity).
Why join us ?
At Infios , we're not just looking for employees; we're looking for partners in innovation, growth, and purpose. Meeting you where you are to create the future you need is at the core of who we are and what we do. Whether you're at the beginning of your career or a seasoned expert, we meet you on your journey, equipping you with the tools and opportunities to build the future you envision. Together, we will relentlessly work toward one common goal \- making supply chains better.
We believe the future is better when supply chains work better.
We are an equal\-opportunity employer and committed to inclusion in the workplace.
At Infios , we believe that inclusion is a fundamental cornerstone of our success. We are committed to creating a safe and welcoming environment where every individual’s unique experiences and perspectives are valued—whether they look, think, move, believe, or love differently.
All qualified applicants will receive consideration for employment without regard to race, color, ethnicity, national origin, sex, sexual orientation, gender identity, marital status, pregnancy, religion, age, disability, veteran status, genetic information, or any other characteristic protected by law.
Reasonable accommodations may be made to enable individuals with disabilities to perform the essential functions of this role. If you require assistance or accommodation due to a disability during the recruiting process, please let us know at [email protected]
Disclaimer: This job advertisement is not designed to cover a comprehensive listing of all duties or responsibilities that are required for this job. Please note that any salary information is a general guideline only. Individual compensation will be determined by various factors such as the scope and responsibilities of the position, experience, education, skills, location, and market and business considerations. Applications must be submitted via our career site.
Salary Context
This $170K-$190K range is above the median for AI/ML Engineer roles in our dataset (median: $180K across 2064 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 3,963 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Infios, 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 $180,000 based on 12,398 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. Disclosed range: $170K to $190K.
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 ($290,000) and AI Safety ($274,200). By seniority level: Entry: $97,760; Mid: $163,400; Senior: $227,400; Director: $244,800; VP: $250,000.
Infios AI Hiring
Infios has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Remote, US. Compensation range: $190K - $190K.
Remote Work Context
Remote AI roles pay a median of $170,000 across 1,883 positions. About 15% of all AI roles offer remote work.
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 3,963 open positions tracked in our dataset. By seniority: 116 entry-level, 1,875 mid-level, 1,532 senior, and 440 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (593 positions). The remaining 3,349 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 ($290,000 median, 39 roles); AI Safety ($274,200 median, 52 roles); Research Engineer ($260,000 median, 421 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 3,963 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,783), Data Scientist (297), AI Software Engineer (279). 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 (116) are outnumbered by mid-level (1,875) and senior (1,532) 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 440 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (593 positions), with 3,349 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 $290,000 median, while Prompt Engineer roles sit at $140,000. 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 (2,043 postings), Aws (1,241 postings), Azure (934 postings), Rag (886 postings), Gcp (774 postings), Pytorch (614 postings), Prompt Engineering (614 postings), Claude (564 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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