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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
Financial Institutions have drastically changed over the last decade, driven by increased regulatory constraints, diverse competition inside and beyond traditional banking organizations, and emerging technologies reshaping long\-standing ecosystems. Sia’s Financial Services Business Unit provides a comprehensive suite of core capabilities designed to address the diverse and evolving needs of our clients, enabling them to navigate complex challenges, seize new opportunities, and achieve their strategic objectives in an increasingly competitive and dynamic business environment.
Job Description
As a Senior Manager supporting our AI Transformation \& Technology service offering, specializing in Agentic AI within banking and capital markets, this role will lead client engagements focused to transform architectural concepts into high\-performing, resilient, production\-grade autonomous agent ecosystems.
The ideal candidate combines strong Agentic AI management capabilities with consulting experience and deep knowledge of banking and capital markets. You will operate at the critical intersection of advanced artificial intelligence, Tier\-1 banking business operations, and rigorous regulatory frameworks.
Responsibilities:
- Client Advisory \& Delivery Leadership: Lead client advisory and end\-to\-end delivery of complex Agentic AI workflows across Capital Markets, Asset Management, Wealth Management, and Corporate Banking, including multi\-agent orchestration, autonomous decision\-making loops, and advanced RAG integration, while translating ambiguous institutional challenges into technical requirements, target operating models, solution blueprints, and strategic execution roadmaps, and managing cross\-functional teams across business, data science, IT infrastructure, risk, and compliance.
- AI Governance, Risk \& Compliance: Lead AI governance, risk, and compliance for agentic AI deployments by aligning solutions to financial services regulatory expectations, including SR 11\-7, OCC guidance, Treasury AI risk management principles, and emerging SEC/EU AI Act requirements, while designing production\-grade observability, monitoring, and auditability frameworks to manage non\-deterministic behavior, hallucination risk, agent\-to\-agent interactions, and Model Risk Management validation.
- Technology Evaluation \& Ecosystem Integration: Advise client executives on AI technology strategy and optimal platform choices, including build\-versus\-buy analyses across enterprise Large Language Models, LLM harnesses, open\-source foundational models, and specialized developer frameworks, while formulating secure and scalable deployment architectures across AWS Bedrock, Azure OpenAI, and GCP Vertex AI with appropriate data ingestion, security, and runtime controls.
- Practice Development \& Commercial Growth: Drive AI Transformation practice growth by developing commercial opportunities, authoring proposals, structuring SOWs, and leading competitive RFP responses, while contributing to Sia’s thought leadership, intellectual property, white papers, and industry forums focused on Agentic AI operationalization in banking.
Qualifications
These qualifications are intended to reflect what may help a candidate succeed as a Senior Manager at Sia. We recognize that candidates may not meet every qualification listed, and we encourage you to apply if you are excited about the role, eager to learn, and believe you can contribute to our teams and clients.
Must\-Have Technical \& Professional Experience:
- Financial Services Expertise: Minimum of 7–10 years of professional experience within Management Consulting or an internal banking transformation function, with an exclusive focus on Tier\-1 Banking, Capital Markets, or Asset /Wealth Management environments.
- Agentic AI Architecture \& Delivery: Proven experience designing and deploying Agentic AI solutions beyond basic chatbots, including multi\-agent orchestration, semantic routing, autonomous workflows, and frameworks such as AutoGen, LangGraph, and CrewAI.
- LLM, Cloud \& Platform Proficiency: Strong understanding of enterprise LLM selection, prompt engineering, fine\-tuning approaches, and integration across major hyperscaler platforms, including Azure, GCP, and AWS Bedrock.
- Observability, Guardrails \& Financial Services Governance: Hands\-on experience with LLM/agent observability, tracing, guardrails, and auditability tools, with working knowledge of Model Risk Management, SR 11\-7, data privacy, security, and financial services regulatory controls.
- Sales \& Business Development: Proven ability to originate new business opportunities and create new revenue streams by identifying client needs, developing relationships, shaping consulting solutions, and converting opportunities into sold work.
Preferred Qualifications:
- Advanced degree (MBA, MS, or PhD) in Computer Science, Data Science, Financial Engineering, or a highly quantitative business discipline.
- Technical certifications across cloud providers (e.g., AWS Certified Solutions Architect, Azure AI Engineer), data platforms (e.g. Databricks) or AI governance programs
Additional Information Compensation \& Benefits
Sia invests in total rewards that support how our people work, live, and grow.
Compensation
- Base Salary: $171,000–$180,000\. Actual compensation within this range will be determined based on experience, qualifications, geographic location, and other job\-related factors permitted by applicable law.
- Annual Performance Bonus
- Personal Sales Bonus (uncapped)
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
At Sia, we believe in fostering a diverse, equitable, and inclusive culture where our employees and partners are valued and thrive in a sense of belonging.
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 $171K-$180K range is above the median for AI/ML Engineer roles in our dataset (median: $175K across 2162 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 4,317 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 $214,900 based on 6,420 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($175K) sits 18% below the category median. Disclosed range: $171K to $180K.
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.
SIA AI Hiring
SIA has 4 open AI roles right now. They're hiring across AI/ML Engineer. Positions span New York, NY, US, San Francisco, CA, US. Compensation range: $116K - $180K.
Location Context
AI roles in New York pay a median of $220,000 across 1,650 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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