Senior Machine Learning Engineer

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: Generative AI Engineer

Experience Level: 3.5 - 7 Years
Location: Bangalore
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.