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About This Role
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ZS is a place where passion changes lives. As a management consulting and technology firm focused on improving life and how we live it, we transform ideas into impact by bringing together data, science, technology and human ingenuity to deliver better outcomes for all. Here you’ll work side\-by\-side with a powerful collective of thinkers and experts shaping life\-changing solutions for patients, caregivers and consumers, worldwide. ZSers drive impact by bringing a client\-first mentality to each and every engagement. We partner collaboratively with our clients to develop custom solutions and technology products that create value and deliver company results across critical areas of their business. Bring your curiosity for learning, bold ideas, courage and passion to drive life\-changing impact to ZS.
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Responsibilities:
- Build, refine, and use ML Engineering platforms and components; develop and implement scalable backend systems, APIs, and microservices using FastAPI.
- Implement MLOps including model KPI measurement, tracking, model drift detection, and model feedback loops.
- Deploy and operationalize ML and Deep Learning models, with a strong focus on LLMs and Generative AI.
- Integrate Azure OpenAI (GPT\-4, GPT\-4 Vision) and other LLM providers with proper retry logic and error handling.
- Maintain up\-to\-date knowledge of state\-of\-the\-art technologies such as LLMs, GenAI, and transformer architectures.
- Scale machine learning algorithms to work on massive data sets under strict SLAs.
- Build and orchestrate model pipelines including feature engineering, inferencing, and continuous model training.
- Write backend application code in Python and SQL using strong object\-oriented principles and asynchronous programming (asyncio, async/await).
- Implement dependency injection patterns and layered architecture (Service, Foundation, Orchestration, DAL).
- Build LLM observability (e.g., Langfuse) to track prompts, tokens, costs, and latency.
- Develop prompt management systems with versioning and fallback mechanisms.
- Implement Celery (or similar) workflows for asynchronous task processing and complex pipelines.
Qualifications:
- Master's or bachelor's degree in Computer Science or a related field from a top university.
- 4\+ years of hands\-on experience in Machine Learning, including production LLM systems.
- Strong fundamentals in machine learning, deep learning, and fine\-tuning models (LLMs), including:
- Understanding of transformer architectures
- Prompt engineering expertise
- Embeddings and vector search
- Experience in backend API design using FastAPI or similar asynchronous frameworks (e.g., Flask, Django), including async patterns and rate limiting.
- Experience with vector databases, including:
- Pinecone, Weaviate, or Chroma
- Embedding storage and similarity search
- Hybrid search implementations
- Strong programming expertise in Python is a must, including:
- Async programming (asyncio, async/await)
- Type hints and Pydantic
- SOLID principles and design patterns
- PySpark/Scala is optional.
- Knowledge of AI/ML concepts and experience integrating AI models into backend services is mandatory.
- Experience with MLOps to measure and track model performance, including:
- MLFlow for model tracking
- Langfuse or similar tools for LLM observability (strongly preferred)
- Model versioning and A/B testing
- Experience working with NLP and computer vision, including:
- Text extraction and preprocessing
- Document understanding (layout, tables)
- OCR processing
- GPT\-4 Vision or similar multimodal integration
- Experience implementing:
- Feature engineering pipelines
- Real\-time inferencing systems
- Batch prediction pipelines
- Model serving with FastAPI
- Experience with ML frameworks, including:
- HuggingFace (transformers, datasets) — mandatory
- Keras/TensorFlow/PyTorch
- LangChain — strongly preferred
- LlamaIndex for RAG
- Familiarity with database technologies such as SQL.
- Good problem\-solving skills and the ability to work in a fast\-paced, team\-oriented environment.
Additional Skills:
- Understanding of DevOps and CI/CD, including:
- Docker containerization
- Azure DevOps pipelines or GitHub Actions
- Kubernetes (nice to have)
- Data security practices, including:
- Multi\-tenant data isolation
- Secure key management (e.g., Azure Key Vault)
- Audit trail implementation
- Experience designing on cloud platforms:
- Azure (strongly preferred): Azure OpenAI, Blob Storage, Key Vault, Container Registry
- AWS or GCP
- Experience with data engineering in Big Data systems, including large\-scale data processing and ETL/ELT pipelines.
- Rate limiting and quota management for high\-throughput API usage.
- Cost management and optimization for LLM usage at scale.
- Document processing expertise (PDF extraction, OCR tooling).
- Production incident management and on\-call experience.
- Testing strategies for non\-deterministic LLM outputs (e.g., golden datasets, fuzzy matching).
- Domain knowledge in regulated industries (e.g., healthcare/pharma workflows, regulatory compliance) is a plus.
- Fluency in English
- Client\-first mentality
- Intense work ethic
- Collaborative spirit and problem\-solving approach
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How you’ll grow:
- Cross\-functional skills development \& custom learning pathways
- Milestone training programs aligned to career progression opportunities
- Internal mobility paths that empower growth via s\-curves, individual contribution and role expansions
Perks \& Benefits:
At ZS, your growth matters. We offer a comprehensive total rewards package that supports your health and well‑being, financial future, time away, and professional development. With robust skills‑building programs, multiple career progression paths, internal mobility, and a deeply collaborative culture, you’ll have the opportunity to do meaningful work, expand your capabilities, and thrive as part of a global community. For details on total rewards in United States, visit ZS US office locations \| Where we work \| ZS.
Hybrid working model:
We are committed to giving our employees a flexible and connected way of working. A flexible and connected ZS allows us to combine work from home and on\-site presence at clients/ZS offices for the majority of our week. The magic of ZS culture and innovation thrives in both planned and spontaneous face\-to\-face connections.
Travel:
Travel is a requirement at ZS for client facing ZSers; business needs of your project and client are the priority. While some projects may be local, all client\-facing ZSers should be prepared to travel as needed. Travel provides opportunities to strengthen client relationships, gain diverse experiences, and enhance professional growth by working in different environments and cultures.
Considering applying?
At ZS, we honor the visible and invisible elements of our identities, personal experiences, and belief systems—the ones that comprise us as individuals, shape who we are, and make us unique. We believe your personal interests, identities, and desire to learn are integral to your success here. We are committed to building a team that reflects a broad variety of backgrounds, perspectives, and experiences. Learn more about our inclusion and belonging efforts and the networks ZS supports to assist our ZSers in cultivating community spaces and obtaining the resources they need to thrive.
If you’re eager to grow, contribute, and bring your unique self to our work, we encourage you to apply.
ZS is an equal opportunity employer and is committed to providing equal employment and advancement opportunities without regard to any class protected by applicable law.
Work Authorization:
This position is not eligible for visa sponsorship. Candidates must have authorization to work in the United States that does not now or in the future require employer sponsorship.
To complete your application:
An on\-line application, including a full set of transcripts (official or unofficial), is required to be considered.
NO AGENCY CALLS, PLEASE.
Find Out More At:
www.zs.com
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 ZS Associates, 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.
ZS Associates AI Hiring
ZS Associates has 2 open AI roles right now. They're hiring across AI/ML Engineer. Positions span South San Francisco, CA, US, Princeton, NJ, US.
Location Context
Across all AI roles, 15% (635 positions) offer remote work, while 3,657 require on-site attendance. Top AI hiring metros: New York (1,650 roles, $220,000 median); San Francisco (1,335 roles, $265,000 median); Los Angeles (708 roles, $214,112 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 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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