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.

If you like wild growth and working with happy, enthusiastic over-achievers, you'll enjoy your career with us!