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About This Role
Company Description
Sia is a next\-generation, global management consulting group. Founded in 1999, we were born digital. Today our strategy and management capabilities are augmented by data science, enhanced by creativity and driven by responsibility. We’re optimists for change and we help clients initiate, navigate and benefit from transformation. We believe optimism is a force multiplier, helping clients to mitigate downside and maximize opportunity. With expertise across a broad range of sectors and services, our 3,000 consultants serve clients worldwide from 48 locations in 19 countries. Our expertise delivers results. Our optimism transforms outcomes.
Strategy \& Management Consulting
Sia’s Strategy \& Management Consulting global footprint and expertise in more than 40 sectors and services allow us to enhance our clients' businesses worldwide. We guide their projects and initiatives in strategy, business transformation, IT \& digital strategy.
Sia supports Energy \& Utilities, Transportation, and Industrials in driving transformation, innovation, and competitiveness. Since 2000, we have guided energy companies (oil \& gas and utilities), transportation, industrial and processes manufacturing leaders through growth strategies, business transformation, digital modernization, energy transition, regulatory challenges, application of AI technologies, and Industry 4\.0 advancements. Our industry subject matter experts help our clients to adapt, innovate, and lead in an evolving landscape.
Job Description
We're looking for an Applied AI Lead to join Sia's ETI practice and help clients move AI, automation, and agent\-based workflows from idea to working production systems.
This is a hands\-on technical role. You will personally build, prototype, and ship AI\-enabled solutions inside client environments, often bridging modern AI tooling with the operational technology (OT) and legacy IT systems common in energy, transportation, and industrial settings. You'll partner closely with our AI Transformation Managers, who own the business case and stakeholder relationships, so you can focus on what you do best: building things that actually work in production, not just in a demo.
You'll join a growing ETI technical team, working day to day alongside AI Transformation Managers and Sia's broader Data \& AI colleagues on live client engagements.
What You'll Do
*Build \& Ship AI Solutions*
- Design, prototype, and build AI agents, copilots, and automation workflows directly inside client systems
- Write production\-quality code to move solutions from pilot to scaled deployment
- Integrate AI tooling with clients' existing data platforms, applications, and, where relevant, operational technology (OT) environments
- Own technical delivery of assigned workstreams end to end, including testing, iteration, and hand\-off to client teams
*Bridge Business and Technical Teams*
- Partner with AI Transformation Managers to translate business requirements into technical specifications and delivery plans
- Work directly with client engineering, data, and IT/OT teams to understand system constraints and integration paths
- Translate technical trade\-offs and constraints back into plain language for business stakeholders
*Solve for Production, Not Just Pilots*
- Evaluate emerging AI platforms, frameworks, and tools for practical fit within client environments, where legacy, high\-reliability, or safety\-critical systems are often the norm
- Build monitoring, evaluation, and guardrails so solutions perform reliably once in production
- Troubleshoot and resolve technical issues that arise during pilot and scale phases
*Contribute to the Practice*
- Build reusable technical accelerators, code libraries, and reference architectures for the ETI practice
- Mentor junior technical talent and contribute to technical hiring and practice development
- Share learnings across engagements to shorten build time on future projects
Qualifications *Required*
- 5\-8\+ years of experience building and shipping software or AI/ML solutions, including production deployment
- Hands\-on experience building with Generative AI: LLMs, AI agents, copilots, retrieval\-augmented generation (RAG), or workflow automation platforms; you should be able to speak to code you've written, not just tools you've used
- Proficiency in Python or another relevant language, plus experience with common AI/ML frameworks and at least one major cloud platform (AWS, Azure, or GCP)
- Experience integrating AI solutions with existing enterprise systems, data platforms, or industrial/OT environments
- Comfort working directly with client technical teams, including engineers, data teams, and IT/OT staff
- Ability to explain technical work and trade\-offs to non\-technical stakeholders
- Bachelor's degree in Computer Science, Engineering, or a related technical field, or equivalent hands\-on experience
*Preferred*
- Experience serving clients in Energy \& Utilities, Transportation \& Logistics, Manufacturing, or Industrial Products, especially where OT/IT integration was part of the work
- Experience with AI platforms and frameworks such as OpenAI, Anthropic Claude, LangChain, LlamaIndex, AWS Bedrock, or Azure AI
- Experience in a consulting or client\-facing technical delivery role
- Familiarity with DevOps/MLOps practices such as CI/CD, monitoring, or model evaluation
Additional Information
Sia invests in total rewards that support how our people work, live, and grow.
Compensation
- Annual Base Salary Ranges, commensurate with experience and qualifications, geographic location, and other job\-related factors permitted by applicable law.
+ Associate Manager Level: $129,500 \- $134,600
+ Manager Level: $140,000 \- $160,000
- Annual Performance Bonus
Benefits
- Medical, dental, and vision; company\-paid life and AD\&D; voluntary supplemental insurance and EAP
- 401(k) with company match and immediate vesting; HSA and FSA options
- College savings and student loan repayment programs
- Paid parental leave; generous PTO, nine company holidays, and one floating holiday
- Cell phone stipend; well\-being and professional development programs
Benefits are subject to applicable plan terms and eligibility requirements.
Workplace
Sia values in\-person collaboration. When serving clients, consultants may be required to work onsite at a client location based on engagement needs. Between client engagements, consultants are expected to work from a Sia office or approved coworking location at least three days per week. Candidates should reside within a reasonable commuting distance of a Sia office or approved coworking location. Sia maintains offices and coworking arrangements across multiple U.S. markets, including New York City, Charlotte, Greater Seattle, San Francisco Greater Bay Area, Los Angeles, Houston, Atlanta, Baltimore, Chicago, Washington D.C.
Work Authorization
Applicants must be legally authorized to work in the United States at the time of application and throughout their employment. This position is not eligible for employment visa sponsorship now or in the future.
Our Commitment
Sia is an equal opportunity employer. All aspects of employment, including hiring, promotion, remuneration, or discipline, are based solely on performance, competence, conduct, or business needs.
Salary Context
This $129K-$160K range is below the median 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 SIA, 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. This role's midpoint ($144K) sits 34% below the category median. Disclosed range: $129K to $160K.
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.
SIA AI Hiring
SIA has 2 open AI roles right now. They're hiring across AI/ML Engineer. Based in Philadelphia, PA, US. Compensation range: $160K - $160K.
Location Context
Across all AI roles, 14% (508 positions) offer remote work, while 3,180 require on-site attendance. Top AI hiring metros: New York (1,045 roles, $220,000 median); San Francisco (810 roles, $277,088 median); Los Angeles (397 roles, $215,000 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 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
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