Senior Machine Learning Engineer

Quantiphi; Bengaluru; On-site; INR 4,000,000 - 6,000,000; Full time

Job summary

Quantiphi is seeking a hands-on Generative AI Engineer for the AI Platform Capabilities team. You will design, build, and deploy enterprise-grade GenAI capabilities across GCP and Azure, closing platform gaps with production-ready components spanning RAG, orchestration, and MLOps.

You will work on Agent Orchestration, LLM Gateways, and GenAIOps, collaborating with LBUs to deliver scalable, secure AI services and end-to-end workflows.

Qualifications

• Hands-on experience building production RAG pipelines with data ingestion, embedding selection and vector indexing.
• Experience building multi-agent systems with orchestration, memory management, and summary generation.
• Experience implementing centralized AI/LLM gateways with routing, caching, observability and policy enforcement.
• Knowledge of GenAIOps, MLOps, prompt engineering, model registry, evaluation pipelines and explainability.
• Experience with CI/CD pipelines and Terraform for provisioning GCP-based AI infrastructure.

Responsibilities

• Design, build, and deploy enterprise-grade GenAI platform capabilities across GCP and Azure.
• Engineer production-ready, reusable GenAI components across the AI stack (Decision, Execution, Build/Lifecycle).
• Close platform capability gaps and deliver API-accessible services with authentication and monitoring.

Skills

Generative AI & RAG engineering

Agentic architecture

LLM gateway & runtime

GenAIOps & MLOps

AgentOps implementation

GCP AI/ML platform

Python & API development

AI safety & governance

CI/CD & IaC

Tools

Vertex AI

Cloud Run

GKE

Cloud Storage

Pub/Sub

BigQuery

Job description

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).

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 servicecoordination.
• 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 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.
• Experience working within large‑scale enterprise programs involving multiple implementation partners and formal governance and change management frameworks.