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!
Senior Machine Learning Engineer
Company Profile
Quantiphi is an award-winning Data Science and Machine Learning Software and Services Company focused on helping organizations translate the big promise of Machine Learning technologies into quantifiable business impact. We were founded on the belief that machine learning and artificial intelligence are transformative technologies that will create the next quantum gain in customer experience and unit economics of businesses. We are one of the five global launch partners for Google in Machine Learning and one of the three global launch partners for the Google Cloud Contact Center AI solution. Our signature approach combines ground-breaking machine-learning research with disciplined cloud and data-engineering practices to create breakthrough impact at unprecedented speed.
We believe in “Solving What Matters”
Company Highlights
● Quantiphi has seen 2.5x growth YoY since its inception in 2013
● Winner of the “Machine Learning Partner of the Year” award from Google for
two consecutive years - 2017 & 2018
● Winner of the “Social Impact Partner of the Year” award from Google in 2019
● Winner of the “Data & Analytics -Specialisation Partner of the Year” and “US
Education -Public Sector Partner of the Year” award for 2020
Role: Senior Machine Learning Engineer
Experience Level: 5–7 Years
Role Summary:
We are seeking a hands-on and technically strong Generative AI Engineer to AI
Platform Capabilities team as part of the Platform Implementation Partner
engagement. In this role, you will design, build, and deploy enterprise-grade
Generative AI platform capabilities across four Local Business Units operating on GCP and Azure.
Your primary focus will be on closing identified AI platform capability gaps by
engineering production-ready, reusable GenAI components across the full AI stack - spanning the Decision & Orchestration Layer (RAG, Agent Orchestration, Semantic Router), the Execution Runtime Layer (LLM Gateway, ML Serving, Tool & Integration Runtime, Event Bus), and the Build & Lifecycle Layer (GenAIOps, AgentOps, MLOps).
You will work closely with the Use Case Implementation Partner and LBU Data & AI teams to ensure that all platform capabilities are built for reuse, comply with
enterprise standards, and are delivered within use case timelines across Agency and Operations domains. This is a deeply technical engineering role focused on building and operationalizing platform components, not managing client engagements.
Required Skills:
● Generative AI & RAG Engineering: Proven, hands-on experience building
production RAG pipelines, including data ingestion, chunking strategy design,
embedding selection, vector indexing (e.g., BigQuery Vector Search), retrieval
logic, and deployment as API endpoints. Strong understanding of RAG
evaluation metrics (Faithfulness, Answer Relevancy, Context Precision/Recall)
and continuous knowledge base updating pipelines.
● Agentic Architecture & Implementation: Demonstrated experience building
multi-agent systems, including Semantic Router, Agent Orchestrator (with
workflow management), Stateful Orchestration Runtime (Reasoning Engine),
and Session State/Memory (LTM/STM) management. Ability to implement
agent-to-agent communication protocols, intent recognition, routing
models, and agent handoff mechanisms with summary generation.
● LLM Gateway & Execution Runtime: Experience implementing centralized
AI/LLM Gateway solutions covering model routing, rate limiting, caching,
observability, fallback logic, and policy enforcement across multiple LLM
providers. Familiarity with Tool & Integration Runtime (API calls, MCP, A2A)
and Event Bus/Messaging architectures for asynchronous, decoupled AI
service coordination.
● GenAIOps & MLOps Frameworks: Hands-on experience implementing
GenAIOps practices including Prompt Engineering, RAG configuration
management, embedding lifecycle management, PEFT/LLM fine-tuning,
Prompt Registry versioning, and LLM evaluation pipelines. Solid understanding
of MLOps principles covering model training, validation, experiment tracking,
model registry, serving, monitoring, and explainability.
● AgentOps Implementation: Experience building and operationalizing
AgentOps frameworks for developing, deploying, monitoring, and governing
AI agents, including scenario testing, approval gate workflows, memory
management, tool call tracking, and latency/success rate monitoring.
● GCP AI/ML Platform Proficiency: Strong, hands-on expertise with GCP
services critical to AI platform delivery, including Vertex AI (Model Garden,
Pipelines, Feature Store, Model Registry), Cloud Run, GKE, Cloud Storage,
Pub/Sub, and BigQuery. Ability to deploy GenAI capabilities as scalable,
standalone API-accessible services.
● Python & API Development: Strong Python programming skills for building
GenAI pipelines, agentic workflows, REST APIs, and automation scripts.
Experience deploying AI services as scalable API endpoints with appropriate
authentication, rate limiting, and monitoring.
● AI Safety, Governance & Compliance: Practical experience implementing AI
safety guardrails, output filtering, PII protection, bias detection, and audit
logging within GenAI platforms. Understanding of data sovereignty
requirements and compliance standards relevant to a regulated financial
services environment.
● CI/CD & Infrastructure as Code: Experience integrating GenAI capabilities into
CI/CD pipelines (GitHub Actions, Jenkins, or Google Cloud Build) for
automated testing, evaluation, and deployment. Working knowledge of
Terraform for provisioning GCP-based AI infrastructure.
Nice-to-Have:
● Experience building AI platform capabilities in a multi-cloud environment
(GCP and Microsoft Azure), ideally supporting a "build once, leverage
everywhere" reusability model across multiple LBUs.
● Familiarity with the Document Intelligence service (AI-powered extraction
from PDFs, invoices, and forms) and Agent Marketplace concepts (centralized
catalog for versioned, reusable AI agents).
● Experience with Knowledge Graph architectures integrated with RAG for
enterprise semantic discovery and relationship-based reasoning.
● Familiarity with RAG orchestration frameworks such as LangChain or
LlamaIndex, and LLM evaluation toolsets such as RAGAS, DeepEval, or Vertex
AI Rapid Eval.
● Experience with Context Store, Vector Store, Embedding infrastructure, and
Feature Store design as components of an AI-ready data layer.
● Knowledge of the financial services or insurance (BFSI) domain, including
data sovereignty, regulatory compliance, and risk management
requirements across APAC markets.
● Google Cloud Professional Machine Learning Engineer certification.
● Experience working within large-scale enterprise programs involving multiple
implementation partners and formal governance and change management
frameworks.
If you like wild growth and working with happy, enthusiastic over-achievers, you'll enjoy your career with us!
Skills Required
- 5-7 years experience in machine learning, AI platform engineering, or related technical roles
- Hands-on experience building production RAG pipelines (data ingestion, chunking, embedding selection, vector indexing, retrieval, API deployment)
- Experience building multi-agent systems (Semantic Router, Agent Orchestrator, stateful orchestration, session state/memory management, agent handoff)
- Experience implementing centralized LLM/AI Gateway solutions (model routing, rate limiting, caching, observability, fallback logic, policy enforcement)
- Hands-on GenAIOps and MLOps (prompt engineering, prompt registry/versioning, embedding lifecycle, PEFT/LLM fine-tuning, model training/validation/serving/monitoring)
- Experience building and operationalizing AgentOps frameworks (scenario testing, approval gates, memory management, tool call tracking, latency/success monitoring)
- Strong, hands-on expertise with GCP AI/ML services including Vertex AI (Model Garden, Pipelines, Feature Store, Model Registry), Cloud Run, GKE, Cloud Storage, Pub/Sub, and BigQuery
- Strong Python programming skills and experience developing scalable REST API endpoints with authentication, rate limiting, and monitoring
- Practical experience implementing AI safety, governance, PII protection, bias detection, and audit logging for GenAI systems
- Experience integrating GenAI capabilities into CI/CD pipelines (GitHub Actions, Jenkins, or Google Cloud Build) and using Terraform for infrastructure provisioning
- Experience building AI platform capabilities in multi-cloud environments (GCP and Azure)
- Familiarity with Document Intelligence services (PDF/form/invoice extraction) and Agent Marketplace concepts
- Experience integrating Knowledge Graphs with RAG for semantic discovery and relationship reasoning
- Familiarity with RAG orchestration frameworks (e.g., LangChain, LlamaIndex) and LLM evaluation toolsets (RAGAS, DeepEval, Vertex AI Rapid Eval)
- Experience designing context stores, vector stores, embedding infrastructure, and feature stores as part of an AI-ready data layer
- Knowledge of financial services/insurance domain (data sovereignty, regulatory compliance across APAC)
- Google Cloud Professional Machine Learning Engineer certification
- Experience working in large-scale enterprise programs with multiple implementation partners and formal governance/change management
Quantiphi Compensation & Benefits Highlights
The following summarizes recurring compensation and benefits themes identified from responses generated by popular LLMs to common candidate questions about Quantiphi and has not been reviewed or approved by Quantiphi.
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Flexible Benefits — Hybrid and work-from-home options are commonly available and perceived as meaningful perks that increase overall package value. Flexibility by team and role often enhances day-to-day experience even when cash pay is not top-tier.
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Healthcare Strength — U.S. materials indicate medical coverage that includes dental and vision, and employee accounts align with having these plans in place. The presence of core health benefits contributes to a baseline of security across key locations.
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Parental & Family Support — Paid parental leave is available in the U.S., with examples citing generous leave lengths. Family-focused policies appear alongside other flexibility features.
Quantiphi Insights
What We Do
Quantiphi is an award-winning AI-first digital engineering company driven by the desire to solve transformational problems at the heart of business. Quantiphi solves the toughest and complex business problems by combining deep industry experience, disciplined cloud, and data-engineering practices, and cutting-edge artificial intelligence research to achieve quantifiable business impact at unprecedented speed.
