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Security Architect

Accenture -
Bengaluru, Karnataka

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Job details

Full-time

Qualifications

CI/CD
Azure
Cloud architecture
Law
Kubernetes
Enterprise Software
CISSP
Git
Google Cloud Platform
Master's degree
CEH
OS Kernels
AWS
Docker
Cloud security
Machine learning
NIST standards
Terraform
Continuous integration
Splunk
Azure Certification
REST
Django
APIs
SIEM
Unit testing
Flask
AI
CompTIA Security+
Python

Full job description

Project Role : Security Architect
Project Role Description : Define the cloud security framework and architecture, ensuring it meets the business requirements and performance goals. Document the implementation of the cloud security controls and transition to cloud security-managed operations.
Must have skills : Secure AI
Good to have skills : NA
Minimum 3 year(s) of experience is required
Educational Qualification : 15 years full time education

Summary:
We are seeking a skilled AI Developer to design, build, and deploy Python-based AI solutions that strengthen our enterprise security capabilities. In this role, you will partner with security, engineering, and cloud teams to translate complex security requirements into intelligent, automated, and scalable solutions. You will leverage modern AI and Generative AI techniques — including Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), and agentic workflows — to drive automation, enhance risk analysis, and streamline security operations across the enterprise.
The ideal candidate is a hands-on developer who thrives at the intersection of AI engineering and cybersecurity, and who can independently take ideas from prototype to production within a cloud-based enterprise environment.

Roles & Responsibilities:

Design, develop, and deploy AI and GenAI solutions (LLMs, RAG pipelines, intelligent agents) tailored to enterprise security use cases such as threat detection, risk analysis, incident triage, and security automation.
Build and maintain robust Python back-end services, APIs, and microservices that power AI-driven security workflows.
Integrate AI solutions with cloud platforms (Azure and/or GCP) and existing enterprise security tools (SIEM, SOAR, vulnerability management, identity platforms, etc.).
Collaborate with security analysts, architects, and stakeholders to translate functional security requirements into scalable, production-grade technical designs.
Develop and optimize data pipelines for ingesting, processing, and embedding security-relevant data for use in RAG and LLM workflows.
Implement responsible AI practices, including prompt engineering, evaluation, guardrails, and monitoring for accuracy, safety, and performance.
Maintain clean, well-tested, and well-documented code, and participate in code reviews and architecture discussions.
Stay current with emerging AI/GenAI techniques, frameworks, and security trends, and proactively recommend improvements.

Professional & Technical Skills:

Strong hands-on experience with Python development, including back-end services, RESTful APIs, and asynchronous programming.
Proficiency with Python frameworks such as FastAPI, Flask, or Django.
Solid understanding of software engineering fundamentals: version control (Git), unit testing, CI/CD, and code quality practices.
Practical experience building solutions with Large Language Models (e.g., OpenAI GPT, Anthropic Claude, Azure OpenAI, Google Gemini, or open-source models like Llama and Mistral).
Hands-on experience designing and implementing Retrieval-Augmented Generation (RAG) pipelines, including chunking strategies, embeddings, and vector databases (e.g., Pinecone, Weaviate, FAISS, Chroma, Azure AI Search).
Experience building AI agents and orchestration workflows using frameworks such as LangChain, LlamaIndex, Semantic Kernel, or LangGraph.
Familiarity with prompt engineering, model evaluation, fine-tuning, and AI safety/guardrail techniques.
Working experience with at least one major cloud platform — Microsoft Azure (Azure OpenAI, Azure Functions, Azure AI Services) or Google Cloud Platform (Vertex AI, Cloud Functions, BigQuery).
Experience integrating with enterprise systems and security tooling such as SIEM (Splunk, Sentinel), SOAR platforms, vulnerability scanners, or identity providers.
Familiarity with containerization (Docker) and orchestration (Kubernetes) for deploying AI workloads.
Experience working with structured and unstructured data, including data preprocessing and transformation.
Understanding of enterprise security concepts: threat detection, risk analysis, access control, secure coding practices, and handling of sensitive data.
Awareness of AI-specific security concerns such as prompt injection, data leakage, and model misuse.
Experience with MLOps / LLMOps tools (MLflow, Weights & Biases, LangSmith).
Exposure to security frameworks such as MITRE ATT&CK, NIST CSF, or OWASP (including OWASP Top 10 for LLMs).
Experience with infrastructure-as-code (Terraform, Bicep).
AI / Cloud: Microsoft Certified: Azure AI Engineer Associate (AI-102), Google Cloud Professional Machine Learning Engineer, Microsoft Azure Data Scientist Associate (DP-100), or AWS Certified Machine Learning – Specialty.
Cloud Platform: Microsoft Certified: Azure Developer Associate (AZ-204), or Google Cloud Professional Cloud Developer.
Security: CompTIA Security+, Certified Ethical Hacker (CEH), CISSP, or Microsoft Certified: Security, Compliance, and Identity Fundamentals (SC-900).

Additional Information:

The candidate should have minimum 3 years of experience in Secure AI.
This position is based at our Bengaluru office.
A 15 years full time education is required.

15 years full time education

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