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Senior Staff Engineer (AI Developer InfraSec Automation)
Nagarro —Mumbai, Maharashtra
- Full-time
- Azure
- Computer Science
- Software troubleshooting
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2h

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Senior Staff Engineer (AI Developer InfraSec Automation)

Nagarro -
Mumbai, Maharashtra

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Job details

Full-time
2 hours ago

Qualifications

BCS
CI/CD
Azure
Law
Computer Science
CSS
Software troubleshooting
Software deployment
CISSP
Git
Google Cloud Platform
CCSP
Java
Master's degree
CEH
AWS
Analysis skills
Docker
Cloud security
Bachelor's degree
Machine learning
JavaScript
Terraform
Continuous integration
Splunk
Back-end development
REST
Scripting
APIs
SIEM
Linux
JSON
Flask
AI
Communication skills
Python
Shell Scripting
Debugging
HTML
Identity & access management
Information Technology

Full job description

Company Description

We're Nagarro.

We are a Digital Product Engineering company that is scaling in a big way! We build products, services, and experiences that inspire, excite, and delight. We work at a scale — across all devices and digital mediums, and our people exist everywhere in the world (18500+ experts across 40 countries, to be exact). Our work culture is dynamic and non-hierarchical. We are looking for great new colleagues. That is where you come in!

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.

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