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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.
Sia’s AI \& Data Business Unit is the powerhouse of our firm’s innovation—merging cutting\-edge Data Science, Generative AI, and advanced digital solutions to transform industries. With over 350 experts worldwide, we tackle projects from proof\-of\-concept to large\-scale deployment, always pushing the boundaries of AI capabilities. Our 12 R\&D labs in Europe and North America drive continuous research in areas like computer vision, MLOps, and deep learning, partnering closely with our business consultants for real\-world impact. By joining Sia’s AI \& Data team, you’ll step into a vibrant, collaborative environment that nurtures professional growth and empowers you to shape the future of AI\-driven consulting.
Job Description
We are growing our Generative AI consulting practice and looking for motivated recent graduates to join as GenAI Consultants. You'll work at the intersection of cutting\-edge AI and real business problems — helping clients across industries design, build, and deploy LLM\-powered solutions that create tangible value.
This is a hands\-on technical role. You'll contribute to the full lifecycle of GenAI projects: from architecture and prototyping through to production deployment, evaluation, and iteration. We invest heavily in your development, and expect you to do the same.
What You'll Do
You'll design and implement GenAI/LLM solutions leveraging models such as Claude, GPT, Gemini, Llama, and Mistral — selecting the right approach (RAG, agents, fine\-tuning, prompt engineering) for each client context. You'll support senior architects in designing scalable, secure, and compliant AI applications while building hands\-on experience across the full stack.
Responsibilities
- LLM/GenAI System Development: Design, build, train, fine\-tune, and deploy sophisticated AI models leveraging LLMs (e.g., GPT\-x, Claude, Gemini, Llama, Mistral) and other generative techniques.
- Assist in Solution Architecture: Support the GenAI Solution Architect in designing robust, scalable, and secure applications.
- Application Development: Develop applications powered by GenAI models (both self\-managed and API\-accessible) that meet business needs and comply with applicable regulations (GDPR, EU AI Act, model licenses, etc.).
- Advanced Prompt Engineering: Design and optimize effective prompts (e.g., few\-shot, Chain/Tree/Graph of Thought, ReAct, Self\-reflection, guardrails), balancing simplicity and complexity to enhance analytical capabilities, refine outputs, improve user experience, and control interactions.
- RAG Implementation: Design and implement Retrieval\-Augmented Generation (RAG) architectures to improve accuracy and relevance by retrieving information from pre\-determined knowledge sources, providing traceability (source attribution).
- Model Selection \& Fine\-Tuning: Select and fine\-tune appropriate models (including multimodal \- VLM, SLM \- Visual Language Models, Small Language Models) to create higher\-quality content (text, image, audio, code, etc.) and maximize business value creation opportunities.
- Integration \& Deployment (MLOps): Implement MLOps best practices for the GenAI lifecycle, including automated pipelines (CI/CD), versioning, monitoring, and maintenance in production environments (Cloud platforms like AWS, Azure, GCP). Ensure seamless integration into existing systems and with external tools/APIs, potentially utilizing standardized protocols (MCP).
- Evaluation \& Responsible AI: Develop and execute rigorous evaluation frameworks to measure model performance, reliability, fairness, and safety. Ensure adherence to Responsible AI principles and help teams and clients navigate end\-to\-end security and compliance processes.
- Research \& Innovation: Stay abreast of the latest advancements in GenAI techniques, technologies, and frameworks. Experiment with new approaches and contribute to internal knowledge sharing.
- Collaboration: Work effectively within cross\-functional teams, communicating complex technical concepts clearly to diverse stakeholders (both technical and non\-technical).
- Documentation: Document processes, methodologies, and best practices for knowledge sharing and future reference.
- Use Case Differentiation: Distinguish between use cases suited for Generative AI versus traditional NLP applications (e.g., NER, sentiment analysis).
Qualifications
- Bachelors Degree Required
- Recent graduate (within 2 years) in CS, Engineering, Data Science, or equivalent
- Strong Python programming skills
- Familiarity with at least one GenAI framework (LangChain, LlamaIndex, or similar)
- Foundational knowledge of NLP concepts, vector embeddings, and semantic search
- Exposure to cloud platforms (AWS, Azure, or GCP)
- Ability to explain technical concepts clearly to diverse audiences
Claude Certified Architect (CCA) Requirement
All candidates must hold — or demonstrate the clear ability to obtain within one week of hire — the Claude Certified Architect – Foundations (CCA\-F) certification from Anthropic.
Preferred Qualifications
- Masters Degree Preferred
- Prior experience building agentic AI systems (LangGraph, AutoGen, etc)
- Familiarity with vector databases (Pinecone, Chroma, pgvector, OpenSearch)
- Experience with or understanding of the Model Context Protocol (MCP)
- Hands\-on fine\-tuning experience with open\-source LLMs
- Familiarity with Docker, Git, and CI/CD workflows
Additional Information Compensation \& Benefits
We believe in supporting our team professionally and personally. Here’s a snapshot of the comprehensive benefits you’ll enjoy as part of Sia.
Competitive Compensation
- Annual Base Salary Range: $80,000\-110,000 annually, commensurate with experience and qualifications
- Annual performance based discretionary bonus
Robust Health Coverage
- 3 Medical plans
- Dental and Vision
- Life, AD\&D and other voluntary insurance
Tax\-Advantaged Accounts
- 401K retirement plan
- 4% matching and 100% vested upon enrollment
- Health Savings Account (HSA)
- Flexible Spending Account (FSA)
- Health, Dependent Care, Commuter
Family Friendly Benefits
- 100% paid parental leave for all new parents with eligible tenure
- Building Healthy Families program if enrolled through Medical plan
Time Off to Recharge
- Generous Paid Time Off (PTO) policy
- 9 company holidays plus 1 floating holiday
Extras that Make Life Easier
- College savings and student loan repayment assistance
- Monthly cell phone stipend
- Access to wellness programs at no cost if enrolled through Medical plan, including:
- Employee Assistance Program at no cost
Diversity, Equity, Inclusion \& Belonging
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. We are committed to recruiting and developing a diverse network of employees and investing in their growth by providing unique opportunities for professional and cultural immersion. Our commitment toward inclusion motivates dynamic collaboration with our clients, building trust by creating an inclusive environment of curiosity and learning which affects lasting impact. Please visit our website for more information.
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.
Office Workplace Guidelines
Sia is committed to providing a flexible workplace environment that supports client, business, and market needs. Consultants located in our primary market office locations—New York City, Charlotte, Seattle, and San Francisco—are expected to live within a reasonable commuting distance and attend the office at least three days or more per week. For Consultants outside of our primary markets, we can offer more flexible in\-person requirements in accordance with your location.
Work Authorization \& Sponsorship
At this time, Sia does not intend to employ any applicant who will require, either now or in the future, employment visa sponsorship or sponsorship for work authorization (i.e., H1\-B visa, F\-1/OPT) or STEM OPT, TN, etc).
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 $80K-$110K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $181K across 1996 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 3,824 AI roles we're tracking, AI/ML Engineer positions make up 71% 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 $178,940 based on 11,900 positions with disclosed compensation. Entry-level AI roles across all categories have a median of $97,380. This role's midpoint ($95K) sits 47% below the category median. Disclosed range: $80K to $110K.
Across all AI roles, the market median is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Safety ($274,200). By seniority level: Entry: $97,380; Mid: $160,000; Senior: $227,400; Director: $243,000; VP: $250,000.
SIA AI Hiring
SIA has 3 open AI roles right now. They're hiring across AI/ML Engineer. Positions span San Francisco, CA, US, New York, NY, US. Compensation range: $110K - $250K.
Location Context
AI roles in New York pay a median of $210,000 across 2,448 tracked positions. That's 5% above the national 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 3,824 open positions tracked in our dataset. By seniority: 119 entry-level, 1,813 mid-level, 1,472 senior, and 420 leadership roles (Director, VP, C-Level). Remote roles make up 16% of the market (613 positions). The remaining 3,187 roles require on-site or hybrid attendance.
The market median for AI roles is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($293,500 median, 31 roles); AI Safety ($274,200 median, 51 roles); Research Engineer ($260,000 median, 401 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 3,824 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,702), Data Scientist (281), AI Software Engineer (258). 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 (119) are outnumbered by mid-level (1,813) and senior (1,472) 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 420 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 16% of all AI roles (613 positions), with 3,187 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 $200,000. Top-quartile roles start at $253,000, and the 90th percentile reaches $307,500. 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 Engineering Manager roles lead at $293,500 median, while Prompt Engineer roles sit at $142,800. 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 (1,968 postings), Aws (1,203 postings), Azure (882 postings), Rag (877 postings), Gcp (735 postings), Prompt Engineering (587 postings), Pytorch (586 postings), Claude (554 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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