Quantiphi
https://quantiphi.comAbout
Quantiphi is an award-winning AI-first digital engineering company driven by the desire to reimagine and realize transformational opportunities at the heart of the business. Since its inception in 2013, Quantiphi has solved the toughest and most complex business problems by combining deep industry experience, disciplined cloud, and data-engineering practices, and cutting-edge artificial intelligence research to achieve accelerated and quantifiable business results.
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Bengaluru, Mumbai, and Trivandrum
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Jobs at Quantiphi
We are seeking a hands-on and technically strong Generative AI Engineer to join the AI Platform Capabilities team as part of a Platform Implementation Partner engagement. In this role, you will design, build, and deploy enterprise-grade Generative AI platform capabilities across multiple Local Business Units operating on both GCP and Azure environments.
The role focuses on engineering production-ready and reusable GenAI components across the full AI stack, including Decision & Orchestration Layers, Execution Runtime Layers, and Build & Lifecycle Layers. You will work closely with implementation partners and Data & AI teams to ensure scalable, enterprise-compliant AI capabilities are delivered within project timelines. This is a deeply technical engineering role with strong emphasis on implementation and operationalization rather than client management.
Key Responsibilities:
- Design and implement enterprise-grade RAG pipelines including ingestion, chunking, embeddings, vector search, retrieval logic, and evaluation frameworks.
- Build multi-agent AI systems with orchestration, semantic routing, memory management, workflow execution, and agent communication capabilities.
- Develop centralized LLM Gateway solutions covering model routing, observability, caching, rate limiting, and policy enforcement.
- Implement scalable GenAIOps, AgentOps, and MLOps frameworks for deployment, monitoring, evaluation, and governance of AI systems.
- Build scalable AI services and REST APIs using Python and deploy them on cloud-native infrastructure.
- Implement AI governance, safety guardrails, audit logging, PII protection, and compliance frameworks.
- Integrate AI solutions into CI/CD pipelines and provision infrastructure using Infrastructure-as-Code practices.
Must-Have Skills:
- Strong hands-on experience with Generative AI, RAG pipelines, embeddings, vector databases, and retrieval evaluation.
- Experience building agentic AI systems including orchestration frameworks, semantic routers, reasoning engines, and memory management.
- Expertise with LLM Gateway implementations, tool integrations, event-driven architectures, and AI runtime systems.
- Solid understanding of GenAIOps, AgentOps, and MLOps principles and tooling.
- Strong experience with GCP AI/ML ecosystem including Vertex AI, Cloud Run, GKE, Pub/Sub, BigQuery, and Cloud Storage.
- Excellent Python programming skills for AI pipelines, APIs, and automation workflows.
- Experience implementing AI governance, safety, compliance, and monitoring frameworks.
- Hands-on experience with CI/CD pipelines and Terraform-based infrastructure automation.
Nice-to-Have Skills:
- Experience working in multi-cloud environments across GCP and Azure.
- Familiarity with LangChain, LlamaIndex, RAGAS, DeepEval, or Vertex AI Rapid Eval.
- Exposure to Knowledge Graphs, Document Intelligence platforms, and Agent Marketplace concepts.
- Experience with enterprise AI-ready data layers including Vector Stores, Feature Stores, and Embedding Infrastructure.
- BFSI domain knowledge including regulatory compliance and data sovereignty considerations.
- Google Cloud Professional Machine Learning Engineer certification.
- Experience working in large-scale enterprise transformation programs.
Machine Learning (ML)We are looking for a hands-on Associate / Architect – Generative AI to design, build, and deploy enterprise-grade GenAI platform capabilities across multiple business units. This role focuses on developing scalable and reusable AI components across the full stack, covering RAG systems, agent orchestration, LLM infrastructure, and GenAIOps on GCP (primary) and Azure.
Key Responsibilities
- Design and build production-ready Generative AI systems and platform components
- Develop and deploy scalable RAG pipelines including data ingestion, embeddings, retrieval, and APIs
- Build agentic AI systems with orchestration, routing, memory, and workflow management
- Develop and manage LLM infrastructure including model routing, caching, observability, and rate limiting
- Build scalable backend services and APIs for AI-driven applications
- Implement GenAIOps/MLOps practices including prompt management, evaluation, monitoring, and deployment
- Work extensively with GCP services such as Vertex AI, BigQuery, Cloud Run, GKE, and Pub/Sub
- Ensure AI governance, safety, compliance, PII protection, and auditability standards are maintained
- Design scalable enterprise AI architectures with strong focus on performance, reliability, and reusability
- Collaborate with cross-functional teams to deliver enterprise-grade AI solutions
- Mentor junior engineers and contribute to technical leadership, architecture discussions, and design reviews
Required Skills & Experience
- Strong hands-on experience building and deploying production-grade Generative AI and RAG systems
- Experience working on multi-agent or agentic AI architectures
- Strong proficiency in Python and backend/API development
- Hands-on experience with GCP AI/ML ecosystem including Vertex AI and BigQuery
- Solid understanding of LLM infrastructure, orchestration layers, and AI platform engineering
- Experience with CI/CD pipelines and Infrastructure as Code tools like Terraform
- Good understanding of GenAIOps/MLOps practices and model lifecycle management
- Strong system design and architecture experience for scalable AI platforms
- Exposure to enterprise application architecture and distributed systems
- Experience leading small engineering teams, mentoring developers, or owning technical delivery is preferred
- Understanding of AI safety, governance, and compliance best practices
Nice to Have
- Experience with LangChain, LlamaIndex, or similar frameworks
- Familiarity with RAG evaluation tools such as RAGAS or DeepEval
- Knowledge of Knowledge Graphs with RAG systems
- Experience working in multi-cloud environments (GCP + Azure)
- Exposure to BFSI or other regulated domains
What We’re Looking For
- Engineers who have built and deployed real-world GenAI systems at scale
- Strong backend engineering and systems-thinking mindset
- Ability to thrive in fast-paced enterprise environments
- Ownership mindset with strong communication and collaboration skills
We are looking for a hands-on Generative AI Engineer to design, build, and deploy enterprise-grade GenAI platform capabilities across multiple business units.
This role focuses on developing scalable, reusable AI components across the full stack—covering RAG systems, agent orchestration, LLM infrastructure, and GenAIOps—on GCP (primary) and Azure.
This is a core engineering role, not a research or client-facing position.
Key Responsibilities
- Design and build production-ready GenAI systems and platform components
- Develop and deploy RAG pipelines (data ingestion, embeddings, retrieval, APIs)
- Implement agent-based architectures (orchestration, routing, memory, workflows)
- Build and manage LLM infrastructure (model routing, caching, rate limiting, observability)
- Develop scalable APIs and services for AI capabilities
- Implement GenAIOps/MLOps practices (prompt management, evaluation, monitoring, deployment)
- Work with GCP services (Vertex AI, BigQuery, Cloud Run, GKE, Pub/Sub) to deploy solutions
- Ensure AI safety, governance, and compliance (PII protection, guardrails, auditability)
- Collaborate with cross-functional teams to deliver reusable, enterprise-grade solutions
Required Skills & Experience
- Strong hands-on experience in Generative AI and RAG systems (production level)
- Experience building multi-agent or agentic AI systems
- Proficiency in Python and backend/API development
- Hands-on experience with GCP AI/ML ecosystem (Vertex AI, BigQuery, etc.)
- Solid understanding of LLM infrastructure and orchestration layers
- Experience with CI/CD pipelines and Infrastructure as Code (Terraform)
- Knowledge of GenAIOps/MLOps practices and model lifecycle management
- Understanding of AI safety, governance, and compliance
Nice to Have
- Experience with LangChain, LlamaIndex, or similar frameworks
- Familiarity with RAG evaluation tools (RAGAS, DeepEval)
- Knowledge of Knowledge Graphs with RAG
- Experience in multi-cloud environments (GCP + Azure)
- Exposure to BFSI/regulated domains
What We’re Looking For
- Engineers who have built and deployed real-world GenAI systems at scale
- Strong backend and systems-thinking mindset
- Ability to work in fast-paced, enterprise environments
PythonWe are seeking a skilled Data Engineer to join the AI Platform Capabilities team supporting the UDP Uplift program.
In this role, you will design, build, and test standardized data and AI platform capabilities across a multi-cloud environment (Azure & GCP).
You will collaborate closely with AI use case teams to develop:
- Scalable data pipelines
- Reusable data products
- Foundational data infrastructure
Your work will support advanced AI solutions such as:
- GenAI
- RAG (Retrieval-Augmented Generation)
- Document Intelligence
Key Responsibilities
- Design and develop scalable ETL/ELT pipelines for AI workloads
- Build and optimize data pipelines for structured & unstructured data
- Enable context processing & vector store integrations
- Support streaming data workflows and batch processing
- Ensure adherence to enterprise data models, governance, and security standards
- Collaborate with DataOps, MLOps, Security, and business teams (LBUs)
- Contribute to data lifecycle management for AI platforms
Required Skills
- 5–7 years of hands-on experience in Data Engineering
- Strong expertise in Python and advanced SQL
- Experience with GCP and/or Azure cloud-native data services
- Hands-on experience with PySpark / Spark SQL
- Experience building data pipelines for ML/AI workloads
- Understanding of CI/CD, Git, and Agile methodologies
- Knowledge of data quality, governance, and security practices
- Strong collaboration and stakeholder management skills
Nice-to-Have Skills
- Experience with Vector Databases / Vector Stores (for RAG pipelines)
- Familiarity with MLOps / GenAIOps concepts (feature stores, model registries, prompt management)
- Exposure to Knowledge Graphs / Context Stores / Document Intelligence workflows
- Experience with DBT (Data Build Tool)
- Knowledge of Infrastructure-as-Code (Terraform)
- Experience in multi-cloud deployments (Azure + GCP)
- Familiarity with event-driven systems (Kafka, Pub/Sub) & API integrations
Ideal Candidate Profile
- Strong data engineering foundation with AI/ML exposure
- Experience working in multi-cloud environments
- Ability to build production-grade, scalable data systems
- Comfortable working in cross-functional, fast-paced environments
NodeJS (Node.js)PythonResponsible for developing, enhancing, modifying, and maintaining chatbot applications in the Global Markets environment. The role involves designing, coding, testing, debugging, and documenting conversational AI solutions, along with supporting activities aligned to the corporate systems architecture.
You will work closely with business partners to understand requirements, analyze data, and deliver optimal, market-ready conversational AI and automation solutions.
Key Responsibilities
- Design, develop, test, debug, and maintain chatbot and virtual agent applications
- Collaborate with business stakeholders to define and translate requirements into technical solutions
- Analyze large volumes of conversational data to improve chatbot accuracy and performance
- Develop automation workflows for data handling and refinement
- Train and optimize chatbots using historical chat logs and user-generated content
- Ensure solutions align with enterprise architecture and best practices
- Document solutions, workflows, and technical designs clearly
Required Skills
- Hands-on experience in developing virtual agents (chatbots/voicebots) and Natural Language Processing (NLP)
- Experience with one or more AI/NLP platforms such as:
- Dialogflow, Amazon Lex, Alexa, Rasa, LUIS, Kore.AI
- Microsoft Bot Framework, IBM Watson, Wit.ai, Salesforce Einstein, Converse.ai
- Strong programming knowledge in Python, JavaScript, or Node.js
- Experience training chatbots using historical conversations or large-scale text datasets
- Practical knowledge of:
- Formal syntax and semantics
- Corpus analysis
- Dialogue management
- Strong written communication skills
- Strong problem-solving ability and willingness to learn emerging technologies
Nice-to-Have Skills
- Understanding of conversational UI and voice-based processing (Text-to-Speech, Speech-to-Text)
- Experience building voice apps for Amazon Alexa or Google Home
- Experience with Test-Driven Development (TDD) and Agile methodologies
- Ability to design and implement end-to-end pipelines for AI-based conversational applications
- Experience in text mining, hypothesis generation, and historical data analysis
- Strong knowledge of regular expressions for data cleaning and preprocessing
- Understanding of API integrations, SSO, and token-based authentication
- Experience writing unit test cases as per project standards
- Knowledge of HTTP, REST APIs, sockets, and web services
- Ability to perform keyword and topic extraction from chat logs
- Experience training and tuning topic modeling algorithms such as LDA and NMF
- Understanding of classical Machine Learning algorithms and appropriate evaluation metrics
- Experience with NLP frameworks such as NLTK and spaCy
The recruiter has not been active on this job recently. You may apply but please expect a delayed response.
Vue.js
AngularJS (1.x)
Angular (2+)
React.js
JavascriptCompany Profile
Quantiphi is an award-winning Applied AI and Big Data software and services company, driven by a deep desire to solve transformational problems at the heart of businesses. Our signature approach combines groundbreaking machine-learning research with disciplined cloud and data-engineering practices to create breakthrough impact at unprecedented speed.
Some 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 and 2018.
- Winner of the "Social Impact Partner of the Year" award from Google for 2019.
- Headquartered in Boston, with 700+ data science professionals across different offices.
For more details, visit: our http://www.quantiphi.com/">Website or our https://www.linkedin.com/company/quantiphi/">LinkedIn Page
Job Description
Role: Associate Tech Architect / Tech Architect – ReactJS +Python+AWS
Experience Level: 7-13 Years
Work location: Mumbai & Bangalore
We are looking for an experienced full stack developer( ReactJS and Python ) who can help create dynamic software applications for our clients with their skill set. In this role, you will be responsible for gathering requirements from clients and accordingly write and test scalable code, and develop front end and back-end components.
Technologies worked on:
ReactJS, Python, AWS
Requirement Description:
- Full Stack developer with experience in ReactJS, Python, API Gateway, Fargate and ECS
- Well-experienced in working with tools like Git, Maven, JFrog
- Should have a solid understanding of object-oriented programming (OOP)
- Well-experienced to perform Unit Testing and Integration Testing and have good experience in Agile based development approach
- Expertise in developing enterprise-level web applications and REST and SOAP APIs using MicroServices, with demonstrable production-scale experience
- Demonstrate strong design and programming skills using JSON, Web Services, XML, XSLT, PL/SQL in Unix and Windows environments
- Strong background working with Linux/UNIX environments and strong Shell scripting experience
- Working knowledge with SQL or No SQL databases
- Understand Architecture Requirements and ensure effective design, development, validation, and support activities
- Understanding of core AWS services, uses, and basic AWS architecture best practices
- Proficiency in developing, deploying, and debugging cloud-based applications using AWS
- Ability to use the AWS service APIs, AWS CLI, and SDKs to write applications
- Ability to identify key features of AWS services
- Identify bottlenecks and bugs, and recommend solutions by comparing the advantages and disadvantages of custom development
- Should contribute to team meetings, troubleshooting development and production problems across multiple environments and operating platforms
- Execute strong collaboration and communication skills within distributed project teams
- Continuously discover, evaluate, and implement new technologies to maximize development efficiency
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