Interested in this AI/ML Engineer role at Ensono?
Apply Now →Skills & Technologies
About This Role
At Ensono, our Purpose is to be a relentless ally, disrupting the status quo and unleashing our clients to Do Great Things*!* We enable our clients to achieve key business outcomes that reshape how our world runs. As an expert technology adviser and managed service provider with cross\-platform certifications, Ensono empowers our clients to keep up with continuous change and embrace innovation.
We can Do Great Things because we have great Associates. The Ensono Core Values unify our diverse talents and are woven into how we do business. These five traits are the key to achieving our purpose – Honesty – Reliability – Curiosity – Collaboration – Passion
About the role and what you’ll be doing: The Junior AI Engineer is the first dedicated engineering hire supporting a new Finance AI Transformation. This function is chartered with a mandate to identify high\-impact workflows across Finance and corporate functions, and release AI\-enabled solutions that deliver measurable outcomes — from accelerating commercial cycles to improving the quality and speed of decision\-making.
This is an applied engineering role. The Junior AI Engineer works alongside the VP, Finance AI Transformation to build production tools that replace manual workflows across Finance, Sales Operations, Procurement, and other corporate functions. Every tool is headless, API\-first, and agent\-callable by default — engineered so a human can use it directly and an AI agent can invoke it as part of a larger workflow. The work happens in an AI\-paired development environment, not a chat interface. Paired development between the engineer and AI tooling is the standard mode of work — successful candidates have already internalized this pattern.
This role is internal\-facing and offers meaningful exposure to senior leaders across the Office of the CFO and adjacent corporate functions. The tools you release feed directly into deliverables reviewed at the CFO level and become part of a compounding catalog of AI\-native solutions across Ensono corporate functions.
What You Will Do:
- Release the function’s pipeline of production tools — prioritized by business impact as the discovery pipeline surfaces them.
- Engage directly with business stakeholders alongside the Solutions Lead — sit in on discovery conversations, ask the technical questions that surface real constraints, and translate what you hear into the right solution form. The Solutions Lead opens the door; the engineer brings the technical eye that decides what actually gets built.
- Build headless, API\-first, and agent\-callable by default — every tool is engineered so a human can invoke it directly and an AI agent can invoke it programmatically as part of a larger workflow. API\-first design, structured I/O, clean tool contracts.
- Pair\-program with an AI\-paired development environment (Claude Code, Cursor, Copilot, or equivalent) — treat AI\-paired development as the baseline mode of work, not an enhancement. Sustained throughput is the load\-bearing promise of this role.
- Contribute to a pattern catalog that compounds — document reusable patterns, tool contracts, and architectural decisions so each solution makes the next one cheaper to build.
- Build internal tooling for the function’s own operations — including, over time, a FinOps agent that monitors AI usage across the function and surfaces cost\-optimization opportunities.
- Engineer with token\-economics in mind — model routing (Haiku for retrieval and classification, Sonnet for reasoning), prompt caching, output validation, retry/cost discipline. Cost\-aware code is good code.
- Partner with Internal IT on the graduation pipeline — work alongside IT to harden tools that prove themselves and ensure handoff readiness when a tool graduates to production\-grade managed infrastructure.
- Practice security\-conscious AI engineering — secrets in Bitwarden, environment hygiene, awareness of data exposure risks, and adherence to internal security and AI Spend Finance policy controls.
- Document workflows, decisions, and reusable patterns so the work compounds across the Finance AI Transformation function rather than living in one person’s head.
We want all new Associates to succeed in their roles at Ensono. That’s why we’ve outlined the job requirements below. To be considered for this role, it’s important that you meet all Required Qualifications. If you do not meet all of the Preferred Qualifications, we still encourage you to apply.
Required Qualifications
- Bachelor’s degree in Computer Science, Software Engineering, Data Science, or a related technical field, or equivalent demonstrated experience. Recent graduates encouraged.
- Demonstrated experience using Claude Code, Cursor, GitHub Copilot Workspace, or an equivalent AI\-paired development environment to build, release, or meaningfully contribute to a working application — not just casual chat\-style use of AI tools.
- Strong Python fluency, including data manipulation, API integration, and writing production\-quality scripts.
- Comfort with at least one additional language: TypeScript / JavaScript, Go, Java, or C\#.
- Working knowledge of API design (REST, JSON I/O, structured output), version control (Git / GitHub), and basic CI/CD concepts.
- Token\-cost awareness when using LLM APIs — understanding of prompt caching, model selection (Haiku vs. Sonnet vs. Opus), and basic optimization techniques.
- Security\-conscious engineering practice — proper handling of API keys and secrets, awareness of data exposure risks, and adherence to internal security practices.
- Strong written and verbal communication, with the ability to engage directly with non\-technical business stakeholders, listen for real constraints, ask clarifying questions, and translate what you hear into clean technical requirements — and into documentation another engineer (or an AI agent) can pick up later. While the function has a dedicated Solutions Lead for discovery, the engineer is expected to participate in stakeholder conversations, not just receive specs.
- Bias toward releasing and driving adoption over experimenting — a track record of finishing things, including in side projects, school projects, or internship work.
Preferred Qualifications
- Hands\-on experience with the Anthropic Claude API, including streaming, tool use, prompt caching, or extended thinking.
- Familiarity with the Model Context Protocol (MCP), agentic tool design, or multi\-step agent workflows.
- Familiarity with multiple AI coding agents and harnesses — comfort moving fluidly between tools rather than being locked into a single environment. Examples include Claude Code (Anthropic), Codex (OpenAI), OpenClaw, Hermes (Nous Research), Goose (Block), Aider, Cline, OpenHands, or opencode.
- Familiarity with running and evaluating open\-weights or open\-source language models locally — examples include Phi (Microsoft, MIT\-licensed), Gemma 4 (Google, Apache 2\.0\), Llama (Meta), Mistral (Apache 2\.0\), Qwen, or DeepSeek — using runtimes such as Ollama, llama.cpp, or vLLM. Awareness of when a local model is the right answer over a frontier API call.
- Experience building data pipelines, ETL, document processing, OCR, or NLP\-based extraction systems.
- Exposure to integrating with enterprise systems — ERP (NetSuite, SAP), CRM (Salesforce), or finance / procurement platforms.
- Open\-source contributions, published research, or portfolio work in applied ML, NLP, or agentic AI.
- Familiarity with FinOps practices, cost\-aware engineering, or per\-use\-case spend tracking.
- Experience designing software for both human and agent consumption — API\-first design, structured outputs, machine\-readable contracts.
- Familiarity with API key and secrets management practices (Bitwarden Secrets Manager, GitHub secrets, environment variable hygiene).
- Experience with cloud infrastructure (AWS, Azure, or GCP), containerization (Docker), or platform\-level deployment patterns.
- Front\-end experience with React (or a comparable framework) for building tool UIs and lightweight internal apps.
Why Ensono?
---------------
Ensono is a place to make better happen – for our clients and for your career. You can do great things through innovation or collaboration, by learning or volunteering, or to promote diversity and inclusion. You can do great things for your own health or for a healthier planet. Whatever it means to you to do great things we want Ensono to be the place you can do it.
We are a client\-facing business, but we do encourage clients to allow us to work remotely most of the time so if you are not required to be on a client site, you can choose to work from home or in our Ensono offices.
Some of our benefits include:
- Unlimited Paid Days Off
- Three health plan options through Blue Cross Blue Shield
- 401k with company match
- Eligibility for dental, vision, short and long\-term disability, life and AD\&D coverage, and flexible spending accounts
- Paid Maternity Leave, Paternity Leave, and Sabbatical Leave
- Education Reimbursement, Student Loan Assistance or 529 College Funding
- Enhanced fertility coverage
- Wellness program
- Flexible work schedule
- Depending on location, ability to take advantage of fitness centers
As of the date of this posting, a good faith estimate of the current pay scale for this role is $100,000 to $138,000 annually based on a full\-time schedule. Please note that placement in the range may vary based on numerous factors including but not limited to skills, experience, internal equity, and business needs. In addition to base salary, other compensation programs include an annual bonus plan based on company and individual performance and an equity grant under our Associate Equity Appreciation Program.
Ensono is an Equal Opportunity/Affirmative Action employer. We are committed to providing equal employment to our Associates and building a diverse and inclusive workforce. All qualified applicants will be considered without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, protected veteran status, disability, or other legally protected basis, in accordance with applicable law.
Pay transparency nondiscrimination statement/posting OFCCP’s pay transparency policy can be found on OFCCP’s website.
If you need accommodation at any point during the application or interview process, please let your recruiter know or email USTalentAcquisition@ensono.com.
Salary Context
This $100K-$138K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $180K across 1841 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 3,708 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Ensono, 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. Entry-level AI roles across all categories have a median of $120,000. This role's midpoint ($119K) sits 46% below the category median. Disclosed range: $100K to $138K.
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.
Ensono AI Hiring
Ensono has 2 open AI roles right now. They're hiring across AI/ML Engineer. Based in Remote, US. Compensation range: $138K - $210K.
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.