Senior Staff Engineer (AI Developer InfraSec Automation)

Job Description Requirements - Experience : 7.5+ years - Strong experience in software engineering, AI/ML development, or applied AI, including experience in building production-grade LLM-based applications. - Strong programming expertise in Python for AI development and automation with hands-on experience in FastAPI or Flask, asynchronous programming, testing frameworks, and package management. - Experience working with LLM providers such as OpenAI, Azure OpenAI, Anthropic, Vertex AI, or similar AI platforms. - Hands-on experience with LLM orchestration frameworks such as LangChain, LlamaIndex, LangGraph, Haystack, or equivalent. - Strong understanding of prompt engineering, structured outputs, JSON schema, function calling, and AI tool orchestration. - Practical experience designing and implementing Retrieval-Augmented Generation (RAG) pipelines using embeddings, chunking strategies, retrieval optimization, and vector databases such as Pinecone, FAISS, Chroma, Weaviate, pgvector, Azure AI Search, or Vertex AI Vector Search. - Experience evaluating LLM performance using frameworks such as RAGAS, DeepEval, Promptfoo, LangSmith, or similar evaluation platforms. - Working knowledge of Java for backend service development and REST API implementation. - Basic frontend development skills using HTML, CSS, and JavaScript. - Strong understanding of Linux, Shell scripting, Git, Docker, CI/CD pipelines, and software deployment practices. - Experience working with at least one major cloud platform such as Microsoft Azure, AWS, or Google Cloud Platform. - Basic understanding of infrastructure security concepts including vulnerabilities, patch management, logging, identity and access management, and security controls. - Familiarity with AI safety concepts including prompt injection attacks, hallucination prevention, data privacy, bias mitigation, and responsible AI practices. - Experience integrating AI solutions with SIEM platforms such as Microsoft Sentinel or Splunk and writing KQL or SPL queries is preferred. - Understanding of Cloud Security Posture Management (CSPM), cloud security controls, IAM policies, WAF, NSGs, and conditional access concepts. - Familiarity with Infrastructure as Code tools such as Terraform or Bicep. - Understanding of vulnerability management concepts including CVE, CVSS, EPSS, CISA KEV, and patch prioritization processes. - Awareness of data privacy regulations such as the Digital Personal Data Protection (DPDP) Act and enterprise data governance practices. - Strong analytical, problem-solving, and debugging skills with the ability to troubleshoot AI models, retrieval pipelines, and security workflows. - Excellent written and verbal communication skills with the ability to collaborate effectively across cross-functional teams. - Bachelor's degree in Computer Science, Information Technology, Engineering, or a related discipline. - Professional certifications such as CISSP (Associate), CEH, CCSP, Google Professional Machine Learning Engineer, AWS Machine Learning Specialty, Azure Administrator (AZ-104), or equivalent cloud and security certifications are an added advantage. Responsibilities - Design, develop, and deploy AI-powered automation solutions to enhance infrastructure security workflows, including vulnerability summarization, log analysis, remediation recommendations, policy reviews, and natural language querying of security data. - Build and optimize LLM-powered AI assistants using prompt engineering, structured outputs, system prompts, and function-calling capabilities. - Design, implement, and maintain end-to-end Retrieval-Augmented Generation (RAG) pipelines, including chunking strategies, embeddings, vector database integration, retrieval optimization, and grounding techniques. - Develop scalable AI services and REST APIs using Python frameworks such as FastAPI or Flask, integrating with commercial and open-source LLM providers. - Build backend services in Java and lightweight frontend components using HTML, CSS, and JavaScript to support AI-driven internal applications and dashboards. - Develop evaluation frameworks, regression test suites, and benchmark datasets to measure LLM accuracy, latency, hallucination rates, and operational costs. - Implement responsible AI practices, including prompt injection protection, PII masking, output filtering, access controls, audit logging, and rate limiting. - Integrate AI solutions with enterprise security tools, vulnerability scanners, SIEM platforms, monitoring systems, and ticketing applications to automate security operations. - Automate AI operational activities including data preparation, embedding refresh, health monitoring, evaluation runs, and deployment processes using Python and Shell scripting. - Test, troubleshoot, optimize, and maintain AI applications to ensure reliability, scalability, performance, and cost efficiency. - Collaborate with infrastructure, security, DevOps, engineering, and data teams to translate business requirements into production-ready AI solutions. - Maintain technical documentation, code repositories, deployment artifacts, API documentation, and operational runbooks. - Continuously evaluate emerging AI technologies, frameworks, and best practices to improve solution quality, security, and developer productivity.