Senior AI Security Engineer

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This is a remote role based in Pune., US Eastern Time (ET), with core hours of 9:00 AM to 5:00 PM ET, Monday through Friday.Full-TimeSenior

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Job Details

Experience: Minimum 5+ years of experience in software engineering, security engineering, platform engineering, DevSecOps, application security, or a related technical discipline.
Required Skills: AWSPythonTerraform

Requirements

Have at least 5 years of experience in software engineering, security engineering, platform engineering, DevSecOps, application security, or a related technical discipline.
Bring hands-on responsibility for production systems supporting real customers.
Have production experience designing, building, deploying, or securing AI-enabled applications, with depth in at least one of agents or agentic workflows, MCP, RAG, or AI application security.
Demonstrate strong software engineering fundamentals, including Python, API design and security, testing, code review, automation, deployment practices, incident response, and configuration management.
Have experience securing cloud-native and distributed systems, including identity and access management, authorization, secrets management, encryption, network and API controls, logging, and monitoring.
Have hands-on AWS or equivalent cloud security architecture experience, including multi-account design, networking, firewalls, compute, key management, secrets management, and centralized logging.
Have Infrastructure as Code experience with Terraform, including policy scanning, OIDC-based CI/CD deploy roles, and repeatable account baselines.
Have experience with graph and vector data stores; Neo4j and Cypher are preferred.
Have practical knowledge of MCP servers and gateways, including remote server authentication, OAuth/OIDC, enterprise SSO, and audit trails.
Have experience designing secure RAG ingestion pipelines, including content chunking, embedding, model governance, index versioning, and sensitive-data detection.
Understand AI security risks and controls, including prompt injection, tool misuse, excessive permissions, sensitive-data exposure, retrieval integrity, and model and dependency risks.
Have working knowledge of secure SDLC and DevSecOps practices, including SAST, SCA, secrets scanning, threat modeling, risk acceptance, and automated deployment gates.
Have experience in regulated or high-trust environments such as healthcare, with working knowledge of relevant security and privacy frameworks.
Be able to work US Eastern Time hours, with core hours of 9:00 AM to 5:00 PM ET, Monday through Friday.
Hold a bachelor's degree in Computer Science, Computer Engineering, Cybersecurity, or a related field.

Responsibilities

Partner with AI engineers and technical leads throughout architecture, implementation, deployment, monitoring, and production support.
Design, build, and secure AI-enabled applications, including agents, agentic workflows, MCP servers and tools, SQL and RAG agents, and retrieval services.
Contribute production-quality Python, security automation, reference implementations, tests, policy-as-code, and integration components.
Define and implement security patterns for identity, authorization, secrets, tenant isolation, data classification, encryption, audit logging, and human approval controls.
Conduct architecture reviews and threat modeling for AI application risks, including prompt injection, insecure tool use, sensitive-data disclosure, and retrieval poisoning.
Build security requirements and automated controls into GitLab CI/CD, code review, testing, deployment, and release processes.
Design private-by-default cloud foundations and secure knowledge graph, retrieval, ingestion, and embedding platforms.
Create secure paved-road templates and coach citizen developers on application registration, scanning, secrets management, logging, and production readiness.
Partner with Privacy, Compliance, and Legal on applicable healthcare and customer-data requirements, and represent the company in customer-facing security discussions.

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