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About This Role
While technology is the heart of our business, a global and diverse culture is the heart of our success. We love our people and we take pride in catering them to a culture built on transparency, diversity, integrity, learning and growth.
If working in an environment that encourages you to innovate and excel, not just in professional but personal life, interests you- you would enjoy your career with Quantiphi!
About Quantiphi:
Quantiphi is an award-winning, AI-First digital engineering and consulting company focused on delivering high-impact Services and Solutions that help organizations solve what truly matters. We partner with enterprises to reimagine their businesses through intelligent, scalable, and transformative AI driving measurable outcomes at the very core of their operations.
Since our founding in 2013, Quantiphi has tackled some of the world’s most complex business challenges by combining deep industry expertise, disciplined cloud and data engineering practices, and cutting-edge applied AI research. Our work is rooted in delivering accelerated, quantifiable business value, not just technology for technology’s sake.
Headquartered in Boston, Quantiphi is a global organization with 4,000+ professionals serving clients across key industry verticals, including BFSI, Healthcare & Life Sciences, CPG, MFG, TME etc.
As an Elite and Premier partner to leading cloud and AI platforms such as NVIDIA, Google Cloud, AWS, and Snowflake, we build and deliver enterprise-grade AI services and solutions that create real-world impact.
Our industry recognition includes:
- 21x Google Cloud Partner of the Year awards in the last 8 years.
- 3x AWS AI/ML award wins.
- 3x NVIDIA Partner of the Year titles.
- 2x Snowflake Partner of the Year awards.
- Top analyst recognitions from Gartner, ISG, and Everest Group.
- Consecutive certifications as a Great Place to Work.
Be part of a trailblazing team that’s shaping the future of AI, ML, and cloud innovation.
Your next big opportunity starts here!
Work Location:USA - Remote
Experience Level:6+ years
Type: Contract (C2C)
Role:
We are seeking an experienced Senior Data & AI Engineer to join our team in. In this role, you will be a key driver in building and modernizing our enterprise data and AI ecosystem. You will architect and deploy scalable real-time streaming pipelines, modern data products, semantic layers, knowledge graphs, and GenAI/Agentic data infrastructure. The ideal candidate blends deep expertise in large-scale data engineering with cutting-edge hands-on skills in AI engineering, RAG architectures, and automated data quality systems to power next-generation business capabilities.
Key Responsibilities
- Senior Data and AI Engineering professional for large and complex data ecosystem leveraging data domains, data products, cloud and modern technology stack
- Real-Time Data Streaming: Design, build and maintain scalable and robust real-time data streaming pipelines using technologies such as Apache Kafka, AWS Kinesis, Spark streaming, or similar.
- Senior Data and AI Engineering professional responsible for Implementing Data and AI pipelines that bring together structured, semi-structured and unstructured data to support AI and Agentic solutions. This Includes pre-processing with extraction, chunking, embedding and grounding strategies to get the data ready.
- Design and Develop Data and AI-driven systems to improve data capabilities, ensuring compliance with industry best practices.
- Design and Develop data domains and data products for various consumption archetypes including Reporting, Data Science, AI/ML, Analytics etc.
- Design and Implement efficient Retrieval-Augmented Generation (RAG) architectures and integrate with enterprise data infrastructure.
- Collaborate with cross-functional teams to integrate solutions into operational processes and systems supporting various functions.
- Stay up to date with industry advancements in GenAI and apply modern technologies and methodologies to our systems. This includes leading prototypes (POCs), conducting experiments, and recommending innovative tools and technologies to enhance data capabilities enabling business strategy.
- Model domain entities, relationships, and business logic in knowledge graphs (e.g., Neo4j, Amazon Neptune, RDF). Integrate data from multiple sources, ensuring canonical representation and semantic consistency.
- Synthetic data generation: Develop and validate synthetic data to simulate rare events and edge cases, supporting robust agent evaluation. Integrate synthetic data workflows with automated testing frameworks to ensure consistent, scalable agent performance assessment.
- Identify and Champion AI driven Data Engineering productivity improvements capabilities accelerating end-to-end data delivery lifecycle. This includes researching and implementing innovative solutions such as AI-driven auto-generation of data pipelines, advanced DevOps practices (AI augmented self-healing data pipelines) for data and automated data quality frameworks.
- Semantic layer and Real time analytics: Design and implement scalable semantic layer with dynamic query translation to deliver real time insights for conversational analytics.
- Integrate the semantic layers with AI/LLM platforms to provide low-latency, secure, and context-rich data access, optimized for high concurrency and aligned with enterprise governance standards.
- Ensure the reliability, availability, and scalability of data pipelines and systems through effective monitoring, alerting, and incident management.
- Implement best practices in reliability engineering, including redundancy, fault tolerance, and disaster recovery strategies.
- Collaborate closely with DevOps and infrastructure teams to ensure seamless deployment, operation, and maintenance of data systems.
- Mentoring junior team members and leading communities of practice to deliver high-quality data and AI solutions while promoting best practices, standards, and adoption of reusable patterns.
- Design and Develop graph database solutions for complex data relationships supporting AI systems, this also includes developing and optimizing queries (e.g., Cyhper, SPARQL) to enable complex reasoning, relationship discovery, and contextual enrichment for AI agents.
- Design and Apply GenAI solutions to insurance-specific data use cases and challenges.
- Partner with architects and stakeholders to influence and implement the vision of the AI and data pipelines while safeguarding the integrity and scalability of the environment.
Skills Required:
- 6+ years of hands-on data engineering experience building large-scale, complex enterprise data ecosystems on cloud platforms (AWS, Azure, or GCP).
- Deep technical expertise in streaming platforms (Apache Kafka, AWS Kinesis, Spark Streaming) and distributed processing frameworks.
- Proven track record in RAG architectures, vector search systems, chunking/embedding techniques, and data pipelines built specifically for LLM and Agentic AI consumption.
- Experience with graph databases (Neo4j, Amazon Neptune) and query languages (Cypher, SPARQL, or Gremlin).
- Proficiency in domain-driven data design, dimensional modeling, semantic layer integration, and building reusable data products.
- High proficiency in Python, Scala, or Java, alongside SQL, DataOps, CI/CD, and containerized deployments.
- Experience in the Financial Industry handling complex, multi-structured domain data is strongly preferred.
- Soft Skills: Exceptional leadership, stakeholder communication, and cross-functional collaboration skills with a track record of mentoring team members.
What is in it for you:
- Be part of the fastest-growing AI-first digital transformation and engineering company in the world
- Be a part of an energetic team of highly dynamic and talented individuals
- Exposure to working with fortune 500 companies and innovative market disruptors
- Exposure to the latest technologies related to artificial intelligence and machine learning, data and cloud
If you like wild growth and working with happy, enthusiastic over-achievers, you'll enjoy your career with us*!*
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 9,653 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At Quantiphi, 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 $212,000 based on 11,857 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200.
Across all AI roles, the market median is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,275; VP: $235,000.
Quantiphi AI Hiring
Quantiphi has 3 open AI roles right now. They're hiring across AI/ML Engineer. Positions span Princeton, NJ, Remote, US.
Remote Work Context
Remote AI roles pay a median of $185,906 across 2,124 positions. About 6% of all AI roles offer remote work.
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 9,653 open positions tracked in our dataset. By seniority: 193 entry-level, 4,500 mid-level, 3,455 senior, and 1,505 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (568 positions). The remaining 9,010 roles require on-site or hybrid attendance.
The market median for AI roles is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 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 9,653 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (7,301), Data Scientist (441), AI Software Engineer (438). 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 (193) are outnumbered by mid-level (4,500) and senior (3,455) 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 1,505 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (568 positions), with 9,010 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 $213,400. Top-quartile roles start at $264,230, and the 90th percentile reaches $319,400. 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 $253,100 median, while Prompt Engineer roles sit at $141,440. 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 (1,605 postings), Aws (916 postings), Azure (724 postings), Rag (634 postings), Gcp (522 postings), Pytorch (495 postings), Kubernetes (384 postings), Claude (383 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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