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

Job description

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.
  
  

Required Skill Profession

Other General