Senior Staff Engineer (AI Developer InfraSec Automation)

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.
• Bachelors 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 :

Bachelors or masters degree in computer science Information Technology or a related field.

Remote Work :

No

Employment Type :

Full-time