Senior Agentic AI Engineer
# Senior Agentic AI Engineer **Alternative Path Private Limited** · United States · `Remote` · `Full Time` 💼 **Уровень роли:** `Senior` 🕒 **Статус:** *Опубликовано: сегодня* · *Источник: Himalayas (JSON API)* --- ### Top Skills & Match 🎯 **Ключевой стек роли:** `[AI-Engineer]` `[Machine-Learning-Engineer]` `[Backend-Engineer]` `[Python-Developer]` `[Software-Engineer]` `[Senior-Agentic-AI-Engineer]` `[Senior-AI-Agent-Engineer]` `[Agentic-AI-Engineer]` `[Agentic-AI-Platform-Engineer]` `[AI-Agentic-Systems-Engineer]` `[Senior-Agentic-Systems-Developer]` `[Principal-Agentic-Engineer]` `[Senior-AI-Engineer]` `[Agentic-Development-Engineer]` --- ### About the Role This is a remote position. Senior AI & Agentic System Engineer Experience: 5+ Years Location: Gurgaon, India / Fully Remote Team: AI Platform / Engineering About the Role We are looking for a Senior AI & Agentic System Engineer to design and build production-grade AI agents that can reason, use tools, interact with APIs, and execute complex multi-step workflows. You’ll work at the intersection of LLM engineering, agent architecture, backend systems, and cloud infrastructure , with significant ownership over architecture, reliability, evaluation, and scalability. Requirements Key Responsibilities - Design and build autonomous AI agents capable of tool use, web/API interaction, memory, and multi-step task execution. - Architect agentic workflows including planner/executor/verifier, multi-agent, HITL, retry, and failure-recovery patterns . - Integrate LLM providers such as OpenAI, Anthropic, and Gemini , including structured outputs, function calling, context management, and prompt optimization. - Build RAG and knowledge pipelines using embeddings, vector databases, hybrid search, reranking, and agent memory. - Develop scalable backend services using Python and/or Node.js/TypeScript , REST/GraphQL APIs, async processing, and background jobs. - Design reliable distributed systems with appropriate patterns for state persistence, caching, idempotency, event-driven workflows, and fault tolerance. - Build observability and evaluation frameworks for AI systems, including tracing, behavioral regression testing, latency, quality, and token-cost monitoring. - Own CI/CD and cloud deployments across development, staging, and production environments. - Evaluate emerging technologies such as MCP, A2A, reasoning models, and agent frameworks and recommend practical adoption. Required Skills - 5+ years of software/backend engineering experience with strong system-design expertise. - Hands-on experience building production AI agents , beyond basic chatbot implementations. - Strong experience with at least one major LLM provider SDK: OpenAI, Anthropic, or Gemini . - Strong understanding of prompt engineering, tool/function calling, structured outputs, context management, and token optimization . - Experience with agent frameworks/patterns such as LangGraph, CrewAI, AutoGen, ReAct, or plan-and-execute . - Strong Python and/or Node.js/TypeScript development skills. - Experience with RAG, embeddings, vector databases, hybrid retrieval, and reranking . - Strong knowledge of REST/GraphQL APIs, asynchronous processing, databases, and distributed systems . - Hands-on AWS experience, particularly Lambda, ECS/Fargate, API Gateway, S3, SQS, SNS/EventBridge, IAM, and CloudWatch . - Experience with Docker, GitHub Actions, and Terraform . - Understanding of AI security, including prompt injection, guardrails, output validation, and safe tool execution . AI/LLMOps & Evaluation Experience with tools such as: - Langfuse, LangSmith, Helicone, or Arize Phoenix - RAGAS, DeepEval, Promptfoo, or Braintrust - LLM tracing, behavioral evaluations, regression testing, and cost monitoring. Nice to Have - Browser automation using Playwright or Puppeteer . - Experience with multimodal/vision-based agents. - Knowledge of GraphRAG and knowledge graphs . - Experience with Temporal or durable workflow execution . - Open-source contributions to AI tooling. - Background in ML/data science alongside software engineering. Tech Stack LLMs: OpenAI, Anthropic Claude Agent Infrastructure: Custom orchestration, MCP Backend: Python, Node.js/TypeScript Cloud: AWS Lambda, ECS/Fargate Data: PostgreSQL, S3, SQS, DynamoDB CI/CD: GitHub Actions Observability: Datadog, CloudWatch, Langfuse IaC: Terraform Browser Automation: Playwright Originally posted on Himalayas
- AI-Engineer
- Machine-Learning-Engineer
- Backend-Engineer
- Python-Developer
- Software-Engineer
- Senior-Agentic-AI-Engineer
- Senior-AI-Agent-Engineer
- Agentic-AI-Engineer
- Agentic-AI-Platform-Engineer
- AI-Agentic-Systems-Engineer
- Senior-Agentic-Systems-Developer
- Principal-Agentic-Engineer
- Senior-AI-Engineer
- Agentic-Development-Engineer
Наблюдалась 2026-10-14, впервые 2026-10-14, источник — Himalayas (JSON API).