Senior AI Engineer

Job Description – Senior AI Engineer (GenAI & Agentic AI) Location: Hyderabad Experience: 5+ Years Total | 2+ Years Hands-on GenAI & Agentic AI Employment Type: Full-time About the Role We are looking for a Senior AI Engineer to drive the design, architecture, development, and productionization of Generative AI, Agentic AI, and AI-driven automation solutions for enterprise cybersecurity products. The ideal candidate should be able to independently own AI initiatives end-to-end—from understanding the business problem and defining the AI approach to architecture, implementation, integration, evaluation, deployment, monitoring, and continuous improvement. The role requires strong hands-on engineering capability rather than research-only or API-integration experience. Key Responsibilities • Own AI/GenAI initiatives end-to-end, translating business and cybersecurity requirements into scalable AI solutions. • Design and implement Agentic AI and Multi-Agent systems for autonomous investigation, decision support, workflow automation, threat analysis, and security operations. • Architect and develop LLM-powered applications, RAG pipelines, AI agents, conversational systems, and intelligent automation workflows. • Design agent orchestration including planning, tool/function calling, memory, state management, task decomposition, retries, human-in-the-loop, and guardrails. • Build enterprise-grade RAG solutions, including document ingestion, preprocessing, chunking, embeddings, vector search, metadata filtering, reranking, and contextual response generation. • Integrate AI agents with enterprise and cybersecurity systems through REST APIs, webhooks, SDKs, databases, SIEM/EDR/security tools, & third-party services. • Establish AI evaluation mechanisms covering accuracy, relevance, groundedness, hallucination, tool-call accuracy, latency, reliability, & cost. • Optimize LLM applications for performance, scalability, reliability, token consumption, latency, and inference cost. • Design secure AI solutions addressing prompt injection, data leakage, access control, sensitive information handling, unsafe tool execution, and AI-specific security risks. • Develop production-ready AI services and APIs using Python/FastAPI, with appropriate logging, error handling, testing, observability, and monitoring. • Independently troubleshoot complex issues across LLM, RAG, agent orchestration, integrations, APIs, databases, and deployment environments. • Mentor AI engineers and contribute to AI architecture standards, reusable frameworks, engineering best practices, and technical decision-making. Must-Have Skills & Experience AI / GenAI / Agentic AI • 5+ years total software/engineering experience, with at least 2+ years hands-on experience in GenAI and Agentic AI. • Strong practical experience building LLM-based production applications and AI agents. • Strong understanding of Agentic AI, Multi-Agent Systems, AI workflows, tool calling/function calling, planning, memory, state management, human-in-the-loop patterns and fine-tuning. • Hands-on experience with LangChain and/or LangGraph; experience with CrewAI, AutoGen, Agno, LlamaIndex or similar frameworks is a plus. • Strong expertise in RAG architecture, embeddings, vector search, hybrid search, reranking, and knowledge-base design. • Hands-on experience with vector databases such as Qdrant, Milvus, Pinecone, Weaviate, Chroma or OpenSearch. • Strong prompt engineering skills, including structured outputs, few-shot prompting, prompt optimization, and context engineering. • Experience with LLM evaluation, hallucination reduction, guardrails, AI observability, and production monitoring. • Experience with LLM providers/models such as OpenAI, Azure OpenAI, Anthropic Claude, Gemini, Llama, Mistral or equivalent. • Strong Python programming skills with FastAPI, Pydantic, async programming, REST APIs, and API integration. • Working knowledge of SQL/NoSQL databases, preferably MySQL/PostgreSQL • Experience with Docker and cloud-based AI deployment, preferably AWS. • Strong understanding of Git, CI/CD, testing, debugging, logging, and production support. Cybersecurity – Preferred • Experience building AI solutions for SOC, SIEM, EDR/XDR, vulnerability management, threat intelligence, incident response, or attack surface management. • Understanding of CVE/CVSS, MITRE ATT&CK, threat intelligence, security alerts, vulnerabilities, and security operations workflows is highly desirable. Good to Have • Basic understanding of JavaScript/TypeScript, React, and modern web application architecture. • Working knowledge of AWS services such as ECS/EKS, Lambda, S3, Bedrock, RDS, OpenSearch, and CloudWatch. • Experience with MCP (Model Context Protocol) and modern agent/tool integration patterns. • Exposure to LoRA/PEFT, open-source LLMs, model serving, or inference optimization. Requirements What We Expect The successful candidate should be able to take an AI initiative from “business problem → AI approach → architecture → POC → production implementation → evaluation → deployment → monitoring” with minimal supervision. We are specifically looking for an engineer who can make architecture decisions, write production-quality code, integrate enterprise systems, troubleshoot independently, and continuously improve AI solutions—not someone limited to prompt writing or calling LLM APIs. Education: B.Tech/B.E./M.Tech in Computer Science, AI/ML, Data Science, or related discipline.