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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.
We’ve been recognized with:
- 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.
- We have also garnered top analyst recognitions from Gartner, ISG, and Everest Group.
- We offer first-in-class industry solutions across Healthcare, Financial Services, Consumer Goods, Manufacturing, and more, powered by cutting-edge Generative AI and Agentic AI accelerators.
- We have been certified as a Great Place to Work for the third year in a row- 2021, 2022, 2023.
Be part of a trailblazing team that’s shaping the future of AI, ML, and cloud innovation.
Your next big opportunity starts here!
For more details, visit: Website or LinkedIn Page.
Preferred Location: Cambridge, MA (USA) The successful candidate will visit client sites, and travel to Cambridge/Pittsburgh will be required.
Experience Level:8+ years
As an Architect MLE at Quantiphi, you will be a senior technical leader responsible for governing the quality, design, and strategic direction of our most critical AI initiatives. This is a highly visible, client-facing architectural role where you will spend 25% to 50% of your time on hands-on prototyping, writing code for complex scenarios, and guiding the engineering team through technical uncertainty.
You will design enterprise-grade, highly scalable, and secure AI/ML platforms that span Generative AI, Agentic systems, traditional ML, and Computer Vision. You will act as the primary technical advisor for strategic client accounts, collaborating with VPs, CTOs, and Chief AI Officers. You will establish practice-wide architectural standards, lead pre-sales solutioning and proposals, and mentor Associate Architects and senior engineers to drive technical excellence at scale.
Responsibilities:
- Enterprise AI Architecture: Design and govern large-scale, mission-critical AI platforms meeting 99.99%+ availability, security, scalability, and compliance requirements in the healthcare and life sciences domain.
- Client Leadership & Solution Pitching: Act as the primary technical contact for strategic accounts. Lead pre-sales efforts, design high-impact solutions, write technical proposals, and pitch them to stakeholders.
- Hands-on Prototyping & Escalation: Maintain active hands-on involvement (25% to 50% of time). Build multi-agentic prototypes, design core frameworks, and step in to write production code or debug complex system issues when the team faces blockers.
- Generative AI & Agentic Platform Design: Architect enterprise-grade agentic systems, multi-agent platforms, autonomous workflow orchestrations, and advanced memory/context management systems leveraging the Model Context Protocol (Server, Client, Gateway).
- Standards & Practice Governance: Establish comprehensive LLMOps and MLOps standards (including prompt versioning, automated evaluation, CI/CD, drift detection, and production monitoring). Define reference architectures and reusable frameworks to accelerate project delivery.
- AI Security & Compliance: Design AI security architectures for regulated industries, ensuring strict compliance with HIPAA, HITRUST, GDPR, SOC 2, and OWASP LLM Top 10 security requirements.
- Mentorship & Practice Growth: Mentor Associate Technical Architects, senior engineers, and delivery teams. Conduct architectural reviews and manage technical debt across engagements.
Required Skills:
Must Have:
- Experience: 8+ years of progressive experience in AI/ML engineering, with at least 3+ years in a technical leadership or architectural role.
- Enterprise Architecture & System Design: + Deep expertise in system integration, legacy modernization, cloud-native design, and API gateway architectures. + Proven ability to visually represent complex, multi-system architectures and explain technical trade-offs with deep, structured reasoning.
- Advanced ML & Deep Learning Foundations: + Mastery of statistical ML, deep learning base architectures (Transformers, CNNs, RNNs), and Computer Vision platforms. + Proficiency in Python and frameworks such as PyTorch or TensorFlow.
- Generative AI & Agentic Platform Architecture: + Deep understanding of the entire agent development lifecycle, multi-agent system design, and A2A (Agent-to-Agent) communication protocols. + Hands-on experience with Agentic AI frameworks (e.g., LangGraph, CrewAI, AutoGen, LlamaIndex, LangChain). + Proven experience with Model Context Protocol (MCP), prompt engineering strategies (Chain of Thought, Tree of Thoughts), and LLM optimization (PEFT, SFT, QLoRA, DPO). + Practical implementation of Vector Databases (e.g., Pinecone, Milvus, Qdrant, OpenSearch).
- Robust MLOps & LLMOps: + Experience designing end-to-end ML lifecycles (data ingestion, feature store, registry, deployment, monitoring, drift detection). + Strong SQL and data transformation experience using BigQuery, Snowflake, Databricks, or Spark.
- AI Security & Regulatory Compliance: + Proven experience designing secure, compliant architectures aligned with major enterprise regulatory frameworks (e.g., SOC 2, GDPR, ISO 27001).
Good to Have:
- Cloud Expertise: Professional Cloud Architect and Machine Learning Engineer certifications on AWS or GCP.
- Thought Leadership: Publications, technical blogs, or conference presentations in Agentic/Generative AI or Enterprise Architecture.
Behavioural Skills:
- Technical Reasoning & Probing: Exceptional ability to justify architectural choices, explain complex technical decisions under deep probing, and evaluate alternative tooling stacks.
- Stakeholder Presence: Exceptional presentation and communication skills. Proven ability to pitch proposals, influence decision-makers, and build strong relationships with key stakeholders.
- Willingness to Walk the Talk: A hands-on leader who leads by example, stepping in to write code, build prototypes, and guide engineers through technical uncertainty.
- Strategic Mentorship: Passion for coaching Associate Architects and senior engineers, establishing standards, and fostering a culture of technical excellence.
What is in it for you:
- Be part of the fastest-growing AI-first digital transformation and engineering company in the world
- Be a leader 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 9,079 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 $211,950 based on 13,048 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 $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($256,000) and Research Engineer ($252,000). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,300; Director: $272,300; VP: $235,830.
Quantiphi AI Hiring
Quantiphi has 3 open AI roles right now. They're hiring across AI/ML Engineer, AI Architect. Positions span Boston, MA, US, Newark, NJ, Princeton, NJ.
Location Context
AI roles in Boston pay a median of $215,000 across 360 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 9,079 open positions tracked in our dataset. By seniority: 106 entry-level, 4,384 mid-level, 3,343 senior, and 1,246 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (531 positions). The remaining 8,478 roles require on-site or hybrid attendance.
The market median for AI roles is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($256,000 median, 66 roles); Research Engineer ($252,000 median, 457 roles); AI Engineering Manager ($251,622 median, 55 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 9,079 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,862), AI Product Manager (453), AI Software Engineer (451). 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 (106) are outnumbered by mid-level (4,384) and senior (3,343) 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 1,246 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (531 positions), with 8,478 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 $213,000. Top-quartile roles start at $263,911, and the 90th percentile reaches $318,400. 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 $256,000 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 (1,649 postings), Aws (898 postings), Azure (723 postings), Rag (671 postings), Gcp (536 postings), Pytorch (434 postings), Claude (410 postings), Kubernetes (406 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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