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About This Role
Description:
AI Platform Engineer
Job Details
Job Type
Full-time, Remote, Occasional travel
IL • Agentic Systems
Description
HALO Branded Solutions is a global leader in branded merchandise, uniform programs, and recognition solutions, and we're building something new. We're assembling a dedicated Agentic Systems team to design and build the intelligent systems that will transform how we take an order from ideation to delivery. That means reimagining the full order management lifecycle: quote capture, supplier and decorator coordination, shipping, PO and invoicing as a platform where AI agents do the work that today requires dozens of manual handoffs across customers, sales, operations, and finance.
We're not experimenting with AI. We're building production systems that will run the business.
The AI Platform Engineer makes the platform real: building, running, and hardening the AI harness (runtime layer), control plane (governance layer), and operations control center (observability layer) in close partnership with the AI Platform Architect.
This is the role that sits between architectural vision and working production infrastructure, implementing the orchestration framework, building the shared state and memory layer, establishing the integration contracts, and ensuring the platform is reliable enough that every agent running on it actually works. The Architect draws the blueprint; you build the building and keep it standing.
This is not a junior role with a senior title. This is a systems-first engineering role with serious development depth. The decisions you make at the implementation level, how state is managed across agent handoffs, how the runtime is structured so deploying a new agent doesn't require rebuilding the platform, how failure is isolated so one agent's problem doesn't cascade, determine whether HALO's agents work in production. That's the responsibility this role carries.
If you're a strong systems engineer with deep distributed systems instincts who is ready to move into AI infrastructure, or an experienced AI platform engineer who wants to build something from the ground up, this is the role.
Responsibilities
Build and operate the orchestration infrastructure.
Implement the multi-agent orchestration layer that all HALO agents run on, managing routing between agents, retry logic, circuit breakers, payload validation, and sequencing. This infrastructure lives outside of individual agents, not inside them. Framework selection is an Architect-level decision; implementation and ongoing operation are yours.
Own shared state and memory.
Design and implement the state management layer that lets agents pass context across handoffs without losing information: session state, cross-agent memory, and the persistence model that supports both short-lived task agents and longer-running workflow agents.
Build the agent runtime environment.
Own the infrastructure agents execute on: deployment pipelines, environment management, model version pinning, and the scaffolding that makes deploying a new agent a configuration exercise, not an infrastructure project. The Agent Developers should be able to ship a new agent without filing a ticket to you first.
Define and enforce integration contracts.
Work directly with the AI Integration Engineer to establish and maintain the contracts governing how agents request data: format, latency guarantees, error handling. Own the API gateway layer between the orchestration platform and the external systems agents interact with.
Own platform reliability.
Uptime, failure isolation, incident response for agent failures, and the runbook for what happens when an agent behaves unexpectedly in production. This is an ongoing operational responsibility. The platform is never "done" in a way that lets you stop thinking about its reliability.
Provide the observability infrastructure.
Implement the platform-level hooks: logging interfaces, trace endpoints, policy enforcement points that the Governance Engineer instruments. The observability standards live with the Governance Engineer; the infrastructure that makes those standards enforceable lives with you.
Requirements:The team you're joining
You'll work directly with the AI Platform Architect (your technical lead and the source of the architectural decisions you implement), the AI Integration Engineer (your closest peer, jointly owning the contract between the orchestration and data layers), the AI Agent Developers (who build on the platform you operate), and the AI Governance & Observability Engineer (who instruments the hooks you provide and dashboards to observe). It's a small, senior, and deliberately collaborative team building infrastructure that doesn't yet exist at HALO.Requirements
- 5+ years of software or platform engineering with genuine depth in distributed systems: state management, failure modes, event-driven architecture, or API platform engineering
- Experience building or operating infrastructure that other engineers build on, platforms with downstream consumers who depend on their reliability
- Production experience with at least one agent or LLM orchestration framework (AWS AgentCore, LangGraph, CrewAI, LangChain, or equivalent) or strong distributed systems instincts and demonstrated ability to ramp quickly
- Comfort with operational ownership and being accountable for reliability
You'll stand out if you have:
- Hands-on experience building multi-agent orchestration infrastructure in production
- Familiarity with agent state and memory patterns: how context persists across agent turns, sessions, and workflow stages
- Experience with AI observability tooling (LangSmith, Arize, Weights & Biases, or equivalent) from the infrastructure side
- Background in API gateway design and integration contract management between platform layers
- Experience in order management, commerce, or supply chain domains
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 HALO, 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.
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.
HALO AI Hiring
HALO has 2 open AI roles right now. They're hiring across AI/ML Engineer. Based in IL, US. Compensation range: $140K - $140K.
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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