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About This Role
About The Role
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This position requires the successful candidate to work on a W2 directly with FDM. We cannot accept C2C, 1099 or employment sponsorship (e.g. H1\-B) for this position.
FDM is a global business and technology consultancy delivering client and industry driven solutions through our 5 core specialist Practices; Software Engineering, Data \& Analytics, IT Operations, Change \& Transformation, and Risk, Regulation \& Compliance.
FDM is seeking a Senior Ai Engineer located in St Petersburg, Floridato support a project in the Financial Servicessector. Involvement in this project is anticipated to last initially 12 Months but may be extended.
This role will be hybrid with requirements to be in office 3 days per week.
About You
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Senior AI Engineer
The Senior AI Engineer supports the adoption and effective use of generative AI across the Investment Bank and Capital Markets \& Advisory functions by designing, building, and improving AI\-powered solutions across banker workflows through custom GPTs, prompts, and emerging agent\-based tools, including AWS AgentCore and AWS Agent Harness. This role blends technical curiosity with practical application and deep business knowledge, contributing to prompt engineering, solution development, and user support while maintaining best practices and documentation. The individual partners with stakeholders to deliver training, troubleshoot issues, and enhance user experience, while also evaluating technology trends and vendors to drive innovation and business value.
Responsibilities
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### Job Description
This role will directly support the Investment Bank and Capital Markets \& Advisory function and its use of generative AI, including GPTs and future agent\-based solutions.
- Support the design, development, testing, and ongoing improvement of GPTs, AI\-powered workflows, and agent\-based solutions, including solutions leveraging AWS AgentCore, AWS Agent Harness, Amazon Bedrock Agents, and other agent orchestration frameworks.
- Assist with prompt engineering, prompt optimization, and documentation of effective prompting patterns and use cases.
- Help maintain standards, best practices, and internal documentation related to AI tools, automation, and innovation platforms.
- Provide hands\-on support to internal users, helping troubleshoot issues and improve overall user experience with AI\-enabled tools.
- Design and deliver role\-specific training to finance professionals.
- Contribute to short\- and long\-term innovation roadmaps through research, analysis, and execution support.
- Analyze technology trends, tools, and vendor offerings to identify opportunities for experimentation or business value.
- Generate insights and recommendations that help improve internal technology offerings and identify areas for growth.
- Assist with data analysis and reporting related to innovation initiatives, pilots, and outcomes.
- Create and maintain presentation materials, including PowerPoint decks, charts, and visuals for leadership and stakeholder updates.
- Facilitate meetings with internal teams and external vendors; capture notes, actions, and follow\-ups.
- Draft concise summaries, reports, or briefing materials on technology topics, vendors, or pilot results.
- Contribute ideas and perspectives to brainstorming sessions and innovation ideation efforts.
Skills
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The ideal candidate is curious, technically inclined, and excited about applying emerging technologies, especially generative AI, to real business problems.
- 4 years of experience in technology, software, digital innovation, or related roles (financial services experience a plus but not required).
- Strong interest in generative AI, automation, and modern software platforms (e.g., LLMs, GPTs, agents, AWS AgentCore, AWS Agent Harness, Amazon Bedrock Agents, APIs, low\-code/no\-code tools).
- Working knowledge of Python/Java and SQL to support data exploration, workflow automation, and AI prototype development; ability to read, modify, and apply scripts with guidance from engineering partners. Deep software engineering experience not required.
- Familiarity with agent orchestration frameworks and AI application architectures, including AWS AgentCore, AWS Agent Harness, Amazon Bedrock Agents, retrieval\-augmented generation (RAG), and multi\-agent workflows.
- Proficiency in data analysis using advanced Excel (e.g., pivot tables, XLOOKUP/VLOOKUP, conditional logic) to support reporting, pilot measurement, and business case development.
- Experience supporting or managing vendors, including coordination around licenses, contracts, or service delivery.
- Working knowledge of project and program management fundamentals.
- Ability to operate effectively in a fast\-paced, evolving environment and adapt quickly as priorities and technologies change.
- Demonstrated ability to think creatively and encourage new approaches or ways of working.
- Strong written communication skills, with the ability to explain technical or complex concepts clearly and concisely.
- Comfortable creating and interpreting charts, diagrams, tables, and other data visualizations.
- Genuine interest in following technology trends, innovation research, and emerging tools.
- Excellent verbal communication and collaboration skills.
- High attention to detail with the ability to manage multiple workstreams simultaneously.
- Team\-oriented mindset with the ability to work across technical and non\-technical stakeholders.
What Success Looks Like
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- High\-value AI use cases are moved from idea to pilot to scaled adoption.
- Solutions are deployed with clear testing, documentation, and governance controls.
- Banker adoption increases and workflows show measurable improvement in speed, quality, or user experience.
- The team develops reusable assets and a repeatable operating model rather than one\-off prompts.
Education
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Bachelor’s degree in computer science, engineering, information systems, data science, finance, economics, or a related field, or equivalent practical experience.
About Us
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About FDM
FDM powers the people behind tech and innovation. We spot trends, find top talent, and help businesses stay ahead.
With 35\+ years of experience, we coach, mentor, and launch fresh thinkers from diverse backgrounds into world\-class careers. Partnering with top global companies, we deliver the right talent at the right time—while guiding our people toward exponential growth.
Global impact – 18 centers across North America, APAC, the UK, and Europe
25,000\+ careers launched – and counting
300\+ trusted client partners
Committed to Equal Opportunities
Tech careers should be for everyone. With 75\+ nationalities represented, FDM thrives on diversity, fuels innovation through unique perspectives, and celebrates success together. As an Equal Opportunity Employer and FTSE4Good\-listed company, we ensure every qualified applicant gets a fair shot—no barriers, just opportunities.
Additional Considerations
FDM Group, Inc. is registered to operate and hire employees in select states within the US. We will consider employment applications exclusively from candidates who are either residing in one of the following states or willing to relocate to them: Arizona, California, Colorado, Connecticut, Delaware, Florida, Georgia, Illinois, Indiana, Massachusetts, Maryland, Minnesota, North Carolina, New Jersey, New York, Pennsylvania, Tennessee, Texas, Utah, and Virginia.
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 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At FDM Group, 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 $214,900 based on 6,420 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
FDM Group AI Hiring
FDM Group has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in US.
Location Context
AI roles in Austin pay a median of $214,343 across 143 tracked positions.
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 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 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 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). 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 (138) are outnumbered by mid-level (2,071) and senior (1,655) 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 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 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 $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. 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 $287,500 median, while Prompt Engineer roles sit at $145,000. 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 (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 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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