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