Generative AI Engineer Data Engineering
Trusys · Gurugram · onsite · Posted 7d ago
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About this role
Company Description Trusys is a safety and governance platform that enables enterprises to deploy AI systems with confidence. It seamlessly integrates with LLM endpoints, voice bots, RAG pipelines, and agentic workflows to provide continuous oversight and control. The platform runs proprietary hallucination detectors, adversarial red-team probes, and drift monitors to identify and mitigate AI risks in real time. Trusys maps every result to industry-standard frameworks such as the EU AI Act, NIST RMF, and MITRE, generating machine-verifiable evidence for audits, compliance, and risk teams. Team members join a growing company focused on building trustworthy AI for complex enterprise environments.
Role Description The Generative AI Engineer – Data Engineering role at Trusys is a full-time, on-site position based in Gurugram. This role involves designing, building, and maintaining data pipelines that power generative AI models and evaluation systems, including ingestion, transformation, and storage of structured and unstructured data. The engineer will collaborate with AI researchers and product teams to integrate data workflows with LLM endpoints, RAG pipelines, and monitoring tools, ensuring performance, reliability, and compliance. Day-to-day activities include implementing scalable data architectures, optimizing data for training and inference, building automation for quality checks, and troubleshooting issues in production environments. The role also includes documenting data processes, contributing to internal tools and frameworks, and participating in code reviews and cross-functional planning.
Qualifications
Role Description The Generative AI Engineer – Data Engineering role at Trusys is a full-time, on-site position based in Gurugram. This role involves designing, building, and maintaining data pipelines that power generative AI models and evaluation systems, including ingestion, transformation, and storage of structured and unstructured data. The engineer will collaborate with AI researchers and product teams to integrate data workflows with LLM endpoints, RAG pipelines, and monitoring tools, ensuring performance, reliability, and compliance. Day-to-day activities include implementing scalable data architectures, optimizing data for training and inference, building automation for quality checks, and troubleshooting issues in production environments. The role also includes documenting data processes, contributing to internal tools and frameworks, and participating in code reviews and cross-functional planning.
Qualifications
- Strong data engineering skills, including experience with data pipelines, ETL/ELT processes, and working with large-scale structured and unstructured datasets.
- Proficiency in programming languages commonly used for data and AI (e.g., Python, SQL) and familiarity with modern data frameworks or libraries.
- Understanding of generative AI concepts, LLMs, RAG architectures, and model evaluation workflows, with the ability to collaborate closely with ML engineers and researchers.
- Experience with cloud platforms and data infrastructure tools (such as data warehouses, distributed storage, orchestration frameworks, or workflow schedulers).
- Knowledge of AI safety, governance, or compliance considerations, and interest in aligning data systems with industry standards and regulatory frameworks.
- Bachelor's or Master's degree in Computer Science, Data Engineering, Information Technology, or a related technical field, or equivalent practical experience.
- Ability to work on-site in Gurugram, collaborate in cross-functional teams, communicate clearly, and take ownership of end-to-end technical deliverables.
- Experience in high-growth or startup environments and prior work on production AI or analytics platforms is a plus.
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Skills
Workflow Schedulersmodel evaluation workflowsmodern data frameworks or librariescloud platformseltGenerative AI conceptsLarge Language Models (LLMs)RAG Architecturesdata warehousesorchestration frameworksdistributed storagePythonSQLETLData Pipeline