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About This Role
It's fun to work in a company where people truly BELIEVE in what they are doing!
We're committed to bringing passion and customer focus to the business.
Fractal is a strategic AI partner to Fortune 500 companies with a vision to power every human decision in the enterprise. Fractal is building a world where individual choices, freedom, and diversity are the greatest assets; an ecosystem where human imagination is at the heart of every decision. Where no possibility is written off, only challenged to get better. We believe that a true Fractalite is the one who empowers imagination with intelligence. Fractal has been featured as a Great Place to Work by The Economic Times in partnership with the Great Place to Work® Institute and recognized as a ‘Cool Vendor’ and a ‘Vendor to Watch’ by Gartner.
Location: Remote (supporting EST hours)
AI Engineer, (Grade 8/7/6):
Role Summary
We are seeking a hands-on AI Engineer with deep, daily experience using and extending AI coding agents to support a large-scale AI-enabled software engineering transformation within a leading Financial Services organization. This role is about building and shipping: using Claude Code, Codex, Cursor, and similar tools to deliver production code, and engineering the configurations, integrations, and automation that make those agents effective on enterprise codebases. The successful candidate will work in an ecosystem built on Claude (including Claude Code and Claude on AWS Bedrock), OpenAI Codex, Cursor, and AWS.
Should be familiar with Coding agents- Claude, Codex and tools like Cursors etc.
Key Responsibilities
Hands-On Delivery with Coding Agents
- Use coding agents (Claude Code, Codex, Cursor, GitHub Copilot, and similar) daily to deliver production-quality code: features, refactors, migrations, tests, and documentation.
- Apply agents to legacy modernization, code migration, and platform migration work on large, multi-repo enterprise codebases.
- Serve as the reference implementer: prove out patterns on real projects, then codify them for other teams.
- Review, test, and harden agent-generated code to production standards.
Agent Configuration & Customization (Build)
- Build and maintain standardized agent configurations: project memory files (CLAUDE.md, AGENTS.md, Cursor rules), custom slash commands, skills, subagents, and hooks.
- Develop MCP servers and tool integrations connecting agents to internal systems (repos, ticketing, CI/CD, documentation, internal APIs).
- Engineer context packages, prompt templates, and codebase indexing approaches tailored to large and legacy environments.
- Tune agent behavior and diagnose failure modes such as context overflow, tool misuse, and looping.
Agent-Driven SDLC Automation
- Build headless and background agent workflows using CLIs and SDKs (Claude Code headless mode, Claude Agent SDK, Codex CLI).
- Implement agent automation in CI/CD pipelines: automated PR review, test generation, dependency upgrades, vulnerability remediation, and migration jobs.
- Build internal tooling and wrappers that make agents easy and safe to run across repos and teams.
- Automate the measurement and evaluation of agent output quality, including tests and regression checks on agent-generated changes.
Security, Governance & Guardrails (Implementation)
- Implement technical controls for safe agent usage: permission models, sandboxing, command allowlists, secrets handling, and data-exposure protections.
- Integrate agents with governed Claude access through AWS Bedrock, IAM, and the enterprise AI Gateway.
- Build audit logging and usage tracking for agent activity and AI-generated code.
- Partner with InfoSec, Risk, and Compliance to translate Financial Services regulatory and model risk requirements into enforceable technical controls.
Telemetry, Evaluation & Tool Benchmarking
- Build telemetry pipelines and dashboards for usage, token consumption, cost, and quality metrics (acceptance rates, cycle time, defect rates).
- Run hands-on bake-offs of emerging coding agents and models against real enterprise tasks; produce data-driven recommendations.
- Stay current on rapidly evolving agentic coding tools and bring working prototypes, not slideware.
Knowledge Sharing (Secondary)
- Document reference implementations, and pair with engineering teams to unblock adoption.
- Contribute reusable configs, templates, and examples to a shared internal repository.
Required Qualifications
- Bachelor's or Master's degree in Computer Science, Engineering, or a related discipline (or equivalent experience).
- 5+ years of professional software engineering experience, with strong proficiency in Python and at least one additional language (e.g., TypeScript, Java).
- Proven, daily hands-on use of Claude Code, Codex, and Cursor or similar agents to deliver production code, with examples of non-trivial work shipped.
- Experience building MCP servers, custom tools, skills, subagents, or hooks.
- Strong grasp of context and prompt design, task decomposition, and how to steer and verify agent output.
- Experience building CI/CD automation and scripting agent workflows via CLIs or SDKs.
- Solid grounding in Git workflows, testing, code review, and secure coding practices.
- Working knowledge of AWS (Bedrock, IAM, Lambda, API Gateway, S3).
- Excellent analytical, problem-solving, and collaboration skills.
Preferred Qualifications
- Experience with the Claude Agent SDK, Claude on Bedrock, or the OpenAI Agents SDK.
- Experience applying coding agents to large-scale legacy modernization or migration programs.
- Experience building internal developer platforms or developer productivity tooling.
- Experience with AI gateways, LLM observability, and usage/cost tracking.
- Experience deploying AI coding tools in a regulated environment.
- Familiarity with SAST/DAST and software supply chain security.
- Infrastructure as code experience (Terraform, CDK).
- Financial Services domain experience across Banking, Capital Markets, Asset Management, Wealth Management, or Insurance.
- AWS certifications (Developer, Solutions Architect, or Machine Learning
Pay: The wage range for this role takes into account the wide range of factors that are considered in making compensation decisions including but not limited to skill sets; experience and training; licensure and certifications; and other business and organizational needs. The disclosed range estimate has not been adjusted for the applicable geographic differential associated with the location at which the position may be filled. At Fractal, it is not typical for an individual to be hired at or near the top of the range for their role and compensation decisions are dependent on the facts and circumstances of each case. A reasonable estimate of the current range is: $100,000 - $130,000. In addition, you may be eligible for a discretionary bonus for the current performance period.
Benefits: As a full-time employee of the company or as an hourly employee working more than 30 hours per week, you will be eligible to participate in the health, dental, vision, life insurance, and disability plans in accordance with the plan documents, which may be amended from time to time. You will be eligible for benefits on the first day of Employment with the Company. In addition, you are eligible to participate in the Company 401(k) Plan after 30 days of employment, in accordance with the applicable plan terms. The Company provides 11 paid holidays and 12 weeks of Parental Leave. We also follow a “free time” PTO policy, allowing you the flexibility to take the time needed for either sick time or vacation. Fractal provides equal employment opportunities to all employees and applicants for employment and prohibits discrimination and harassment of any type without regard to race, color, religion, age, sex, national origin, disability status, genetics, protected veteran status, sexual orientation, gender identity or expression, or any other characteristic protected by federal, state or local laws.
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Salary Context
This $100K-$130K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $178K across 1623 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 9,079 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At Fractal Analytics, 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. This role's midpoint ($115K) sits 46% below the category median. Disclosed range: $100K to $130K.
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.
Fractal Analytics AI Hiring
Fractal Analytics has 2 open AI roles right now. They're hiring across AI/ML Engineer, MLOps Engineer. Positions span NJ, US, New York, NY, US. Compensation range: $125K - $130K.
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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