Staff Consultant – GDS Cyber, Frontier AI Layered Defense

Staff / Junior AI Security Engineer supporting the design and implementation of security for AI-enabled systems. Working with senior engineers to address frontier AI risks and safeguards in enterprise environments.

Posted 7/11/2026full-timeBengaluru • 🇮🇳 IndiaLeadWebsite

Core Competencies

Role fit

Core Competencies

Use this summary to align your resume positioning with the role.

Demonstrates foundational knowledge in AI/ML concepts, cybersecurity principles, and secure coding practices while supporting the implementation and validation of AI applications. Proficient in using frameworks like LangChain and OpenAI APIs, with a focus on ensuring data protection and compliance in AI system integrations.

Highest-signal resume keywords

AI/ML ConceptsPython ScriptingSQL Data HandlingCybersecurity PrinciplesCloud Environment Exposure

ATS Keywords

Tailor your resume

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills

AI Application ValidationData ProcessingSecure Coding PracticesAutomation ScriptingVersion ControlCI/CD PipelinesPrompt HandlingResponse ConstraintsData Leakage AwarenessVulnerability Management

Soft Skills

Strong Communication SkillsAttention to DetailWillingness to Learn

Tools & Technologies

LangChainLangGraphLlamaIndexAutoGenOpenAI APIsAzureAWSGCP

Industry Keywords

Layered Defense ControlsRAGAgent WorkflowsStructured DataUnstructured DataAPI SecurityIAMData Protection Requirements

Tech Stack

Tools & technologies

AWSAzureCloudCyber SecurityGoogle Cloud PlatformPythonSQL

About the role

Key responsibilities & impact

Support implementation of layered defense controls for LLM, RAG, and agentic AI use cases, including input handling, context isolation, tool-use boundaries, response checks, access controls, and monitoring.
Assist in building and testing AI applications using frameworks such as LangChain, LangGraph, LlamaIndex, AutoGen, OpenAI-compatible APIs, and related orchestration tools.
Configure and validate basic AI application safeguards, including prompt handling, response constraints, sensitive data handling checks, and escalation paths for uncertain or high-impact outputs.
Support secure RAG implementation by helping validate data ingestion, retrieval boundaries, embedding and vector store access, source attribution, and secure handling of structured and unstructured enterprise data.
Execute predefined misuse-resistance and scenario validation checks, including attempts to bypass instructions, expose hidden context, trigger unintended actions, or produce unsafe or unreliable outputs.
Review AI system logs, traces, prompts, outputs, tool calls, and telemetry to identify anomalies, unexpected behavior, and potential security issues for escalation.
Support secure integration of AI systems with enterprise APIs, identity platforms, cloud services, workflow tools, and knowledge repositories under senior guidance.
Assist in documenting validation results, control observations, implementation notes, remediation actions, and reusable delivery patterns.
Contribute to automation scripts, test harnesses, and repeatable playbooks for AI application validation and continuous monitoring.
Follow secure coding practices, data protection requirements, internal standards, and responsible technology expectations while working on AI applications and integrations.
Stay current on emerging frontier AI risks, AI application security patterns, resilience testing methods, and layered defense practices, and apply learnings to project delivery.

Requirements

What you’ll need

0–3 years of experience in software development, cybersecurity, AI/ML, data engineering, cloud engineering, or related academic/project work
Foundational understanding of AI/ML concepts, including LLMs, prompts, embeddings, tokens, vector databases, RAG, and basic agent workflows
Familiarity with Python and basic scripting for automation, testing, data processing, or API integration
Working knowledge of SQL and basic data handling concepts, including structured and unstructured data sources
Awareness of AI application risks such as unintended information exposure, data leakage, unreliable outputs, unsafe tool use, insecure integrations, model misuse, and over-permissive automation
Foundational knowledge of cybersecurity concepts including authentication, authorization, IAM, API security, secrets handling, secure coding, logging, and vulnerability management
Exposure to cloud environments such as Azure, AWS, or GCP, with basic understanding of secure deployment and access configuration
Familiarity with AI or application development frameworks such as LangChain, LangGraph, LlamaIndex, AutoGen, OpenAI APIs, or comparable tools is preferred
Basic understanding of CI/CD pipelines, version control, software testing, and secure software development lifecycle practices
Ability to follow structured validation plans, implement predefined controls, document observations, and escalate risks clearly
Strong communication skills, attention to detail, and willingness to learn in a fast-evolving frontier AI security domain.

Benefits

Comp & perks

Competitive salary
Flexible working hours
Professional development
Work from home options