Interested in this AI/ML Engineer role at MicroAgility?
Apply Now →Skills & Technologies
About This Role
About the Role
We are hiring a hands on AI Practice Lead to design, build, and scale production grade Agentic AI systems for enterprise clients, with a focus on regulated industries including financial services. This is a player coach role. You will architect and code the core platform yourself, and you will shape the practice, its playbooks, and its people as it grows. You will work directly with client executives, technical stakeholders, and internal delivery teams to take AI initiatives from strategy through deployment.
What You Will Do
- Design and build end to end Agentic AI systems that integrate large language models with enterprise data, APIs, and workflows.
- Architect and implement multi agent workflows using frameworks such as LangGraph, AutoGen, CrewAI, Semantic Kernel, or equivalent, choosing the framework to fit the client problem rather than defaulting to one stack.
- Build and maintain reusable harnesses for prompt testing, agent evaluation, regression, and performance monitoring across releases.
- Implement observability, tracing, guardrails, security controls, and automated evaluation pipelines that meet enterprise and regulatory standards.
- Lead client discovery, use case shaping, and technical due diligence, translating business problems into agentic architectures with clear ROI.
- Establish the practice's technical standards, reference architectures, delivery playbooks, and hiring bar as the team scales.
- Partner with sales and account teams on solutioning, RFP responses, and executive presentations.
What You Bring
- 10\+ years of software or ML engineering experience, with at least two years hands on with LLM based systems in production.
- Demonstrated ability to build and ship agentic systems, not just prototype them. Public repositories, published architectures, or client case studies preferred.
- Strong working experience with at least two of LangGraph, AutoGen, CrewAI, Semantic Kernel, LangChain, or LlamaIndex.
- Deep integration experience across REST and GraphQL APIs, event driven systems, vector databases, and enterprise platforms (Salesforce, ServiceNow, SAP, or similar).
- Hands on experience building evaluation harnesses, including offline eval sets, online metrics, human in the loop feedback, and automated regression.
- Practical grasp of observability tooling (OpenTelemetry, Langfuse, Arize, Weights and Biases, or similar) and of guardrail and safety approaches (input and output filtering, tool use policies, PII redaction, prompt injection defence).
- Exposure to regulated industries, ideally financial services, with a working understanding of data handling, audit, and compliance expectations.
- Strong executive presence and the ability to lead technical conversations with CIOs, CTOs, and business sponsors.
Bonus Experience
- Prior founder, practice lead, or head of AI role at a services firm or product company.
- Contributions to open source agent frameworks or evaluation tools.
- Experience with retrieval architectures beyond basic RAG, including Graph RAG, hybrid retrieval, and knowledge graph work.
- Domain background in FP\&A, risk, KYC and AML, fraud, or other financial services functions.
Pay: From $100\.00 per hour
Benefits:
- Flexible schedule
Application Question(s):
- Have you personally built and shipped Agentic AI systems using LangGraph, AutoGen, CrewAI, Semantic Kernel, or a similar framework?
- Have you built reusable harnesses for testing, evaluating, or monitoring AI agents (including offline eval sets, automated regression, or human in the loop feedback)?
Experience:
- AI models: 8 years (Required)
- LLM Based Systems: 7 years (Required)
Work Location: Remote
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 3,708 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At MicroAgility, 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 $218,750 based on 3,817 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $230,000.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
MicroAgility AI Hiring
MicroAgility has 2 open AI roles right now. They're hiring across AI/ML Engineer. Based in Remote, US.
Remote Work Context
Remote AI roles pay a median of $185,334 across 717 positions. About 14% 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 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 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 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). 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 (102) are outnumbered by mid-level (1,705) and senior (1,469) 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 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 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 $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. 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 $300,000 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 (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 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.