Senior Machine Learning Engineer

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.