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About This Role
ECI is hiring senior-level Builder Developers (Senior Agentic Software Engineers) focused on rewriting how enterprise software is built, tested, and operated using AI.
The Mission: Use AI to build autonomous agents and workflows that transform legacy ERP systems into modern, AI-driven platforms. You are not maintaining old code, you are building the future. If you enjoy 4-hour "alignment meetings" and writing Jira tickets for things an agent could do in 3 seconds, you’re going to hate it here.
Builder is not just a title; it is a measure of autonomy, architectural complexity, and "Agentic Leverage." While a traditional "Senior Software Engineer" might be focused on completing tickets, a Builder is focused on automating workflows and owning the execution of AI-driven modules.
Here are the Builder levels we are hiring:
- BUILDER 2 (Senior): Focuses on Agentic Implementation. This is the "Engine Room" of the team. They don't just use AI; they build the agents. They can take a legacy module and build a LangGraph or CrewAI flow to modernize it with minimal supervision.
- BUILDER 3 (Lead): Focuses on Systemic Orchestration. They design the "Guardrail Layers" and the "Eval Frameworks." They ensure that the agents built by the team are deterministic, cost-effective, and scalable.
- BUILDER 4 (Principal/Architect): Focuses on The Meta-Platform. They build the internal "Builder Tools" that the other three levels use. They are looking at the 12-month horizon of model capabilities and ensuring the ERP architecture is ready for "Autonomous Operation."
Core DNA (Shared Foundation)
- AI-Native Workflow: Use AI tools to accelerate development, testing, and documentation 10x faster than traditional methods.
- Agentic Implementation: Build, debug, and deploy autonomous agents (LangGraph, AutoGen, Playwright-LLM hybrids) that navigate complex ERP schemas and business logic.
- Legacy-to-Modern Bridge: Treat 20-year-old legacy systems as puzzles for AI-driven modernization.
- Deterministic Mindset: Build for 100% reliability with automated validation, evals, and guardrails.
What You’ll Actually Do
- Build Execution Agents: Move beyond RAG into Action. Build agents that can navigate an ERP schema, identify a data discrepancy, and suggest a fix.
- Automate the "Untestable": Use AI to wrap legacy business logic in modern unit tests and documentation.
- Prompt Engineering & Evals: Use frameworks like DSPy to move from "vibes-based" prompting to programmatic, optimized instructions.
- Rapid Prototyping: Turn a "what if" into a working AI-driven feature in days, not months.
The Stack
Python, SQL (Postgres/SQL Server), TypeScript, OpenAI/Anthropic, pgvector/Qdrant, LangChain/LangGraph, dbt, Apache Hop/Airflow, Docker, Weights & Biases, OpenTelemetry. And, any other tool that helps us achieve greatness.
Who You Are
- The "Early Adopter" Archetype: You were using GPT-3 before it was cool and had a local Llama running the week it leaked. You spend more time on r/LocalLLaMA, GitHub Trending, and X (AI Twitter) than on LinkedIn.
- Agent-First Thinker: You don't think in "functions"; you think in "capabilities." You’ve built at least one autonomous agent or complex LLM chain that actually does something useful.
- The "Manual Task" Hater: You have a physical reaction to repetitive work. If you have to do a task twice, you’ve already started drafting the Python script or agentic workflow to automate it.
- Technically Fearless: You aren't intimidated by spaghetti legacy code. You see a 20-year-old undocumented ERP module as a goldmine for AI-driven refactoring and modernization.
- Rigor over Hype: While you love the latest models, you care about Evals. You know the difference between a "cool demo" and a "production-grade agent" that handles financial data with 100% accuracy.
- The Tribe Member: You want to work in an environment where your "AI hacks" and custom automation scripts are celebrated as your greatest contribution, not hidden as "side projects."
Why Join Us?
- The "Final Boss" of Complexity: ERPs are the ultimate challenge for AI. If you can automate a General Ledger or a global supply chain, you’ve mastered the hardest problem in software.
- Agentic Autonomy: We don't do "tickets." We do missions. You’ll have the freedom to choose the models, frameworks, and agent architectures you believe will win.
- The BUILDER Tribe: You won't be the "only AI person" in the room. You’ll be part of a 4-person squad of like-minded enthusiasts who are all obsessed with moving 10x faster.
- No Red Tape: If the agent works and the evals pass, we ship. We don't have a "Change Management Committee." We have a "Does It Work?" policy.
- The Final Boss: ERP data is the hardest problem in software. If you want an easy life, go build another photo-sharing app. If you want to win the game, let’s go!
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 11,520 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At ECI Software Solutions, 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 $212,040 based on 13,780 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 $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($266,050) and AI Engineering Manager ($253,461). By seniority level: Entry: $112,320; Mid: $193,200; Senior: $230,000; Director: $272,250; VP: $235,830.
ECI Software Solutions AI Hiring
ECI Software Solutions has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Remote, US.
Remote Work Context
Remote AI roles pay a median of $188,200 across 2,430 positions. About 6% 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 11,520 open positions tracked in our dataset. By seniority: 140 entry-level, 5,630 mid-level, 4,176 senior, and 1,574 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (708 positions). The remaining 10,737 roles require on-site or hybrid attendance.
The market median for AI roles is $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($266,050 median, 70 roles); AI Engineering Manager ($253,461 median, 58 roles); Research Engineer ($252,000 median, 476 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 11,520 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (8,769), AI Software Engineer (586), AI Product Manager (511). 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 (140) are outnumbered by mid-level (5,630) and senior (4,176) 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 1,574 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (708 positions), with 10,737 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 $213,480. Top-quartile roles start at $264,000, 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 $266,050 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 (2,267 postings), Aws (1,178 postings), Azure (972 postings), Rag (924 postings), Gcp (715 postings), Pytorch (586 postings), Claude (572 postings), Kubernetes (559 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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