Data & AI Engineer – Cyber Risk Intelligence Platform – India/Remote

Data & AI Engineer – Cyber Risk Intelligence Platform – India Location:India (Remote) About Quantara AI & the Role Quantara AI is a next-generationCyber Risk Intelligence and Governanceplatform that helps CISOs, Boards, and executive teamsquantify, prioritize, and communicate cyber risk in business terms . Our AI-powered solution combinesCyber Risk Quantification (CRQ)andContinuous Threat Exposure Management (CTEM)to automate compliance, identify the top 1% of exposures that truly matter, and deliver insights that drive measurable business resilience. We are seeking a highly skilledData & AI Engineerto help design and scale the data and AI backbone of our platform. This role involves developinglarge-scale data pipelines , buildingAI/LLM-powered systems , and implementingenterprise-grade backend and orchestration architecturesthat support data-driven decision-making. You will work on end-to-enddata and AI infrastructure , includingETL/ELT development, LLM orchestration, API engineering, and metric computation —helping evolve a scalable, secure, and intelligent enterprise platform. Key Responsibilities 1. Data Engineering & Architecture Design, build, and maintainenterprise-scale data pipelinesfor structured, semi-structured, and unstructured data. Developdata acquisition and transformation workflowsintegrating multiple APIs and business data sources. Create and optimizerelational and analytical data modelsfor performance, scalability, and reliability. Establishdata quality, validation, and governance standardsacross ingestion and analytics workflows. Enablereal-time and batch processing pipelinessupporting large-scale enterprise applications. 2. AI/LLM Development & Orchestration Design, develop, and deployLLM-driven and agentic AI applicationsfor analytics, automation, and reasoning. BuildRetrieval-Augmented Generation (RAG)pipelines and knowledge orchestration layers across enterprise data. Fine-tune and trainlanguage modelsusing modern open-source frameworks and libraries. ImplementNLP and conversational AI components , including chatbots, summarization, and question-answering systems. Optimizemodel orchestration, embeddings, and context managementfor scalable AI inference. 3. Backend Development & API Engineering Develop and manageRESTful APIs and backend servicesto support AI, analytics, and data operations. Implementsecure API access controls , error handling, and logging. Buildmicroservices and event-driven architecturesto deliver modular, reliable data and AI capabilities. Integrate backend components with data pipelines, analytics engines, and external systems. 4. Metrics Computation & Quantification Design automated engines for computingrisk, ROI, RRI, maturity, and performance metrics . Integrate quantification logic into business and risk data models to provide real-time visibility. Develop scalabledata and AI computation frameworksthat support executive reporting and analytics. Collaborate with product and data teams to ensure metric accuracy, transparency, and explainability. 5. CI/CD, Deployment & Cloud Operations Implement and manageCI/CD pipelinesfor testing, deployment, and environment management. Work withcloud-native technologiesfor infrastructure automation, monitoring, and scaling. Usecontainerization and orchestration toolsfor consistent, portable, and secure deployment. Establish performance monitoring, observability, and alerting across production systems. Qualifications 6–10 yearsof experience indata engineering, backend development, or AI platform engineering . Proven success inproduct development environmentsand experience buildingenterprise-grade SaaS applications . Strong programming proficiency inPythonor equivalent languages for backend and data systems. Deep understanding ofSQLand relational databases, including schema design and performance tuning. Experience buildingETL/ELT pipelines , API integrations, and data orchestration workflows. Hands-on experience withAI and LLM technologies(e.g., Transformers, RAG, embeddings, vector databases). Familiarity withMLOps and LLMOps concepts , including model deployment, scaling, and monitoring. Practical experience with technologies such as: Data frameworks: Airflow, dbt, Spark, Pandas, Kafka, Kinesis Cloud & DevOps: AWS, GCP, Azure, Terraform, Docker, Kubernetes Databases: PostgreSQL, MySQL, Snowflake, BigQuery, DynamoDB AI/LLM: LangChain, Hugging Face, OpenAI API, LlamaIndex, Weaviate, Pinecone, FAISS CI/CD: Jenkins, GitHub Actions, GitLab CI, or similar tools Strong knowledge ofdata security, scalability, and performance optimizationin production systems. Preferred Skills Background incybersecurity, risk analytics , orfinancial data systemsis a plus. Experience withagentic AI systems ,autonomous orchestration , orconversational analytics . Understanding ofdata governance, metadata management , andcompliance automation . Exposure tostreaming data systemsandreal-time analytics architectures . Ability tomentor junior engineersand contribute to design and architectural discussions.Compensation Competitive India market base salary + performance-based incentives. Open toContract-to-Hire (CTH)with potential for full-time conversion based on performance.