Associate Staff Engineer

Requirements Experience: 4+years Experience in software development, AI/ML engineering, or applied AI, including at least 1–2 years of hands-on experience building production-grade LLM applications. Expert-level proficiency in React.js for developing scalable, responsive, and reusable frontend applications. Strong hands-on experience with Python for AI development, automation, backend services, and API integration. Working knowledge of Java for backend services, REST API development, and enterprise application integration. Practical experience with LLM providers and APIs, including OpenAI, Anthropic, Azure OpenAI, Vertex AI, or equivalent platforms. Hands-on experience with prompt engineering, system prompts, structured outputs, JSON schemas, function calling, and tool integration. Strong understanding of LLM application development, AI assistants, agentic workflows, and enterprise AI integration. Experience with at least one LLM orchestration framework such as LangChain, LlamaIndex, LangGraph, Haystack, or equivalent. Hands-on experience implementing RAG pipelines, including embeddings, document chunking, retrieval tuning, grounding, and vector database integration. Experience with at least one vector database or search platform, such as pgvector, Pinecone, Weaviate, Chroma, FAISS, Azure AI Search, or Vertex AI Vector Search. Working knowledge of LLM evaluation methodologies, including evaluation datasets, regression testing, and frameworks such as RAGAS, DeepEval, Promptfoo, or LangSmith. Experience with FastAPI, Flask, asynchronous programming, Python packaging, and testing frameworks. Basic frontend development knowledge of HTML, CSS, and JavaScript, along with backend API development experience. Knowledge of Shell scripting, Linux fundamentals, Git, CI/CD pipelines, and Docker. Exposure to at least one cloud platform such as Microsoft Azure, AWS, or Google Cloud Platform. Basic understanding of Infrastructure Security concepts, including vulnerabilities, patch management, security logs, IAM, and access controls. Awareness of responsible AI and security practices, including prompt injection, data leakage, hallucinations, bias, and content filtering. Familiarity with SIEM platforms such as Microsoft Sentinel or Splunk, including the ability to write KQL or SPL queries for AI-assisted security detection and investigation. Basic understanding of Cloud Security Posture Management (CSPM) and cloud security controls such as Network Security Groups (NSGs), Web Application Firewalls (WAF), IAM policies, and conditional access. Understanding of Infrastructure as Code (IaC) tools such as Terraform or Bicep for cloud-native AI service deployment and configuration. Exposure to data privacy and regulatory requirements applicable in India, including the DPDP Act and RBI IT Risk Framework, particularly in handling personal and sensitive data within AI systems. Familiarity with vulnerability management concepts, including CVE, CVSS, EPSS scoring, CISA KEV catalogue, and patch prioritization workflows. Strong analytical and problem-solving skills, with the ability to debug LLM behavior across prompts, retrieval pipelines, and tool integrations. Ability to interpret evaluation metrics, application logs, and security telemetry to improve AI quality, reliability, and operational efficiency. Demonstrated ownership of AI solutions from prototype to production, including evaluation, observability, testing, and documentation. Strong learning agility and adaptability to evolving AI models, frameworks, tools, and development practices. Excellent written and verbal communication skills, with the ability to collaborate effectively across technical and business teams. Certifications such as CISSP, Associate of ISC², CEH, Google Professional Machine Learning Engineer, AWS Certified Machine Learning – Specialty, Microsoft Azure Administrator Associate (AZ-104), CCSP, or Google Associate Cloud Engineer are preferred. Responsibilities Design, develop, and deploy LLM-powered features, AI assistants, and intelligent automation solutions for Infrastructure Security (InfraSec) workflows. Build AI-driven capabilities for vulnerability summarization, log triage, remediation recommendations, security policy reviews, configuration assessments, and natural-language querying of security data. Develop scalable, responsive, and reusable frontend applications using React.js for AI-powered internal tools and dashboards. Build and maintain backend AI services and APIs using Python, with Java-based backend development wherever required. Integrate LLM providers such as OpenAI, Anthropic, Azure OpenAI, Vertex AI, and open-source models into enterprise applications. Design and optimize prompt templates, system prompts, structured-output schemas, and function-calling workflows. Implement end-to-end Retrieval-Augmented Generation (RAG) pipelines, including document chunking, embeddings, vector store integration, retrieval optimization, and response grounding. Develop AI agents and tool-calling workflows using frameworks such as LangChain, LlamaIndex, LangGraph, Haystack, or equivalent technologies. Build evaluation frameworks, golden datasets, and regression test suites to measure LLM accuracy, hallucination rates, response quality, latency, and cost. Continuously improve prompt engineering and AI outputs through offline and online evaluations. Implement responsible AI and security controls, including prompt-injection protection, PII redaction, output filtering, rate limiting, audit logging, and access management. Integrate AI services with vulnerability scanners, ticketing platforms, SIEM solutions, monitoring tools, and other security infrastructure to enable automated workflows. Automate AI/ML operations activities such as data preparation, embedding refresh, evaluation runs, health checks, and monitoring using Python and Shell scripting. Test, debug, and optimize AI applications to ensure reliability, scalability, performance, and cost efficiency. Maintain technical documentation, source code repositories, deployment artifacts, and operational procedures. Collaborate with security, infrastructure, engineering, DevOps, and data teams to translate business requirements into practical AI-driven solutions. Apply infrastructure security principles and cloud security controls while designing and deploying AI services. Continuously explore emerging LLM models, frameworks, prompting techniques, and evaluation methodologies to improve solution quality and security posture. Bachelor’s or master’s degree in computer science, Information Technology, or a related field.