Interested in this AI/ML Engineer role at Arctiq?
Apply Now →Skills & Technologies
About This Role
Company Overview:
Arctiq is a global, intelligence\-driven technology services company delivering professional and managed services across Hybrid Cloud Infrastructure, Networking \& Connected Experiences, Cybersecurity, Data \& AI, Autonomous Operations \& Intelligence, and Enterprise Service Management. We help organizations operate, secure, and modernize complex environments by unifying infrastructure, networking, data, security, automation, and observability under a single, integrated operating model. Our work focuses on helping customers reduce operational friction, improve resilience, and make better, faster decisions as their environments evolve. Arctiq builds on decades of industry expertise and a customer\-centric ethos to deliver exceptional value to clients across diverse industries.
This is a remote, contract opportunity for one of Arctiq’s clients. This team works EST hours.
Position Overview:
We are seeking a highly skilled Agentic AI Engineer to lead the design, development, deployment, and support of next\-generation intelligent automation solutions. This role is focused on building production\-grade AI agents and agentic workflows that automate complex business processes, integrate with enterprise systems, and deliver measurable business outcomes.
Working closely with business stakeholders, architects, data teams, and developers, you will identify opportunities where AI agents can improve operational efficiency, accelerate decision\-making, and transform business processes. You will be responsible for taking AI solutions from concept through production, ensuring they are scalable, secure, observable, and maintainable.
Responsibilities:
- Design, develop, deploy, and maintain Agentic AI solutions that automate business processes and complex workflows.
- Build autonomous and semi\-autonomous AI agents capable of reasoning, planning, tool usage, retrieval, memory management, and multi\-step task execution.
- Develop and deploy multi\-agent systems that collaborate to accomplish business objectives.
- Integrate Large Language Models (LLMs) from providers such as OpenAI, Anthropic, Google, Azure OpenAI, and open\-source alternatives.
- Build Retrieval\-Augmented Generation (RAG) solutions leveraging vector databases and enterprise knowledge sources.
- Design and implement agent orchestration frameworks using technologies such as LangGraph, LangChain, CrewAI, AutoGen, Semantic Kernel, LlamaIndex, or similar platforms.
- Develop APIs, microservices, and backend services that support AI\-driven applications and workflows.
- Integrate AI agents with enterprise systems including CRM, ERP, ticketing platforms, collaboration tools, databases, and third\-party SaaS applications.
- Implement robust observability, monitoring, evaluation, logging, guardrails, and governance frameworks for AI solutions.
- Collaborate with business stakeholders to identify automation opportunities and determine where AI agents can deliver measurable value.
- Evaluate emerging AI technologies and recommend practical approaches for implementation.
- Perform hands\-on development using Agile and Scrum methodologies.
- Continuously optimize deployed AI systems for performance, reliability, cost efficiency, and user adoption.
- Troubleshoot production AI applications and rapidly resolve operational issues.
- Serve as a technical advisor on AI automation strategy, architecture, and implementation best practices.
Qualifications:
- 5\+ years of software engineering experience with modern application development.
- 1\+ years of hands\-on experience building and deploying Generative AI or Agentic AI solutions in production environments.
- Demonstrated experience designing, developing, and deploying AI agents that interact with APIs, databases, enterprise systems, and external tools.
- Proven experience building autonomous workflows using agent orchestration frameworks such as LangGraph, LangChain, CrewAI, AutoGen, Semantic Kernel, LlamaIndex, or equivalent technologies.
- Strong Python development experience and familiarity with AI application development ecosystems.
- Experience integrating commercial and open\-source LLMs into production applications.
- Strong understanding of prompt engineering, agent design patterns, tool calling, memory management, context management, and AI workflow orchestration.
- Experience implementing Retrieval\-Augmented Generation (RAG) architectures and vector database solutions.
- Experience working with cloud platforms such as Azure, AWS, or GCP.
- Experience with Docker, containerized deployments, CI/CD pipelines, and modern DevOps practices.
- Ability to interpret SDK documentation and rapidly implement integrations with new platforms and services.
- Excellent problem\-solving skills with the ability to decompose complex business challenges into scalable AI\-driven solutions.
- Strong communication skills with the ability to explain technical concepts to both technical and non\-technical audiences.
- Experience deploying AI solutions on Azure OpenAI, AWS Bedrock, Vertex AI, or similar enterprise AI platforms.
- Experience with MCP (Model Context Protocol) implementations and AI tool ecosystems.
- Bachelor's degree in Computer Science, Software Engineering, MIS, or related field.
Arctiq is an equal opportunity employer. If you need any accommodations or adjustments throughout the interview process and beyond, please let us know. We celebrate our inclusive work environment and welcome members of all backgrounds and perspectives to apply.
*We thank you for your interest in joining the Arctiq team! While we welcome all applicants, only those who are selected for an interview will be contacted.*
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 3,823 AI roles we're tracking, AI/ML Engineer positions make up 69% of the market. At Arctiq, 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 $181,170 based on 12,692 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $165,000.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
Arctiq AI Hiring
Arctiq has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Philadelphia, PA, US.
Location Context
Across all AI roles, 15% (590 positions) offer remote work, while 3,217 require on-site attendance. Top AI hiring metros: New York (2,643 roles, $211,000 median); San Francisco (2,168 roles, $253,000 median); Los Angeles (1,792 roles, $191,580 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 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 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 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), 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 (112) are outnumbered by mid-level (1,798) and senior (1,516) 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 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 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 $200,100. Top-quartile roles start at $253,500, 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 $275,000 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 (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 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
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.