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

Full 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 ThreatExposure 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

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.