Senior ML 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__!_