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-generation Cyber Risk Intelligence and Governance platform that helps CISOs, Boards, and executive teams quantify, prioritize, and communicate cyber risk in business terms . Our AI-powered solution combines Cyber Risk Quantification (CRQ) and Continuous 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 skilled Data & AI Engineer to help design and scale the data and AI backbone of our platform. This role involves developing large-scale data pipelines , building AI/LLM-powered systems , and implementing enterprise-grade backend and orchestration architectures that support data-driven decision-making. You will work on end-to-end data and AI infrastructure , including ETL/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 maintain enterprise-scale data pipelines for structured, semi-structured, and unstructured data. Develop data acquisition and transformation workflows integrating multiple APIs and business data sources. Create and optimize relational and analytical data models for performance, scalability, and reliability. Establish data quality, validation, and governance standards across ingestion and analytics workflows. Enable real-time and batch processing pipelines supporting large-scale enterprise applications. 2. AI/LLM Development & Orchestration Design, develop, and deploy LLM-driven and agentic AI applications for analytics, automation, and reasoning. Build Retrieval-Augmented Generation (RAG) pipelines and knowledge orchestration layers across enterprise data. Fine-tune and train language models using modern open-source frameworks and libraries. Implement NLP and conversational AI components , including chatbots, summarization, and question-answering systems. Optimize model orchestration, embeddings, and context management for scalable AI inference. 3. Backend Development & API Engineering Develop and manage RESTful APIs and backend services to support AI, analytics, and data operations. Implement secure API access controls , error handling, and logging. Build microservices and event-driven architectures to 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 computing risk, ROI, RRI, maturity, and performance metrics . Integrate quantification logic into business and risk data models to provide real-time visibility. Develop scalable data and AI computation frameworks that 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 manage CI/CD pipelines for testing, deployment, and environment management. Work with cloud-native technologies for infrastructure automation, monitoring, and scaling. Use containerization and orchestration tools for consistent, portable, and secure deployment. Establish performance monitoring, observability, and alerting across production systems. Qualifications 6–10 years of experience in data engineering, backend development, or AI platform engineering . Proven success in product development environments and experience building enterprise-grade Saa S applications . Strong programming proficiency in Python or equivalent languages for backend and data systems. Deep understanding of SQL and relational databases, including schema design and performance tuning. Experience building ETL/ELT pipelines , API integrations, and data orchestration workflows. Hands-on experience with AI and LLM technologies (e.g., Transformers, RAG, embeddings, vector databases). Familiarity with MLOps and LLMOps concepts , including model deployment, scaling, and monitoring. Practical experience with technologies such as: Data frameworks: Airflow, dbt, Spark, Pandas, Kafka, Kinesis Cloud & Dev Ops: AWS, GCP, Azure, Terraform, Docker, Kubernetes Databases: Postgre SQL, My SQL, Snowflake, Big Query, Dynamo DB AI/LLM: Lang Chain, Hugging Face, Open AI API, Llama Index, Weaviate, Pinecone, FAISS CI/CD: Jenkins, Git Hub Actions, Git Lab CI, or similar tools Strong knowledge of data security, scalability, and performance optimization in production systems. Preferred Skills Background in cybersecurity, risk analytics , or financial data systems is a plus. Experience with agentic AI systems , autonomous orchestration , or conversational analytics . Understanding of data governance, metadata management , and compliance automation . Exposure to streaming data systems and real-time analytics architectures . Ability to mentor junior engineers and contribute to design and architectural discussions. Compensation Competitive India market base salary + performance-based incentives. Open to Contract-to-Hire (CTH) with potential for full-time conversion based on performance.