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 SaaS 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 & 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 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.