*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.
Additional Information
The candidate should have minimum 3 years of experience in Secure AI.
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\).