Associate Architect - Machine Learning

Role Title: Generative AI Engineer

Experience: 6–9 Years

Location: Bangalore (3 days WFO)

Role Summary: We are seeking a hands-on and technically strong Generative AI Engineer to join our AI Capabilities team.

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 GCP cloud environment, ideally supporting a build once, leverage everywhere reusability model across multiple LBUs.

● 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