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.

Qualifications

Bachelor’s or master’s degree in computer science, Information Technology, or a related field.