Senior Agentic AI Engineer
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Job description
This is a remote position.
Senior AI & Agentic System Engineer
Experience:5+ Years
Location:Gurgaon, India / Fully Remote
Team:AI Platform / EngineeringAbout the Role
We are looking for aSenior AI & Agentic System Engineerto 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 ofLLM 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 includingplanner/executor/verifier, multi-agent, HITL, retry, and failure-recovery patterns.
- Integrate LLM providers such asOpenAI, Anthropic, and Gemini, including structured outputs, function calling, context management, and prompt optimization.
- BuildRAG and knowledge pipelinesusing embeddings, vector databases, hybrid search, reranking, and agent memory.
- Develop scalable backend services usingPython 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 asMCP, A2A, reasoning models, and agent frameworksand recommend practical adoption.
Required Skills
- 5+ years of software/backend engineering experience with strong system-design expertise.
- Hands-on experience buildingproduction AI agents, beyond basic chatbot implementations.
- Strong experience with at least one major LLM provider SDK:OpenAI, Anthropic, or Gemini.
- Strong understanding ofprompt engineering, tool/function calling, structured outputs, context management, and token optimization.
- Experience with agent frameworks/patterns such asLangGraph, CrewAI, AutoGen, ReAct, or plan-and-execute.
- StrongPython and/or Node.js/TypeScriptdevelopment skills.
- Experience withRAG, embeddings, vector databases, hybrid retrieval, and reranking.
- Strong knowledge ofREST/GraphQL APIs, asynchronous processing, databases, and distributed systems.
- Hands-on AWS experience, particularlyLambda, ECS/Fargate, API Gateway, S3, SQS, SNS/EventBridge, IAM, and CloudWatch.
- Experience withDocker, GitHub Actions, and Terraform.
- Understanding of AI security, includingprompt 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 usingPlaywright or Puppeteer.
- Experience with multimodal/vision-based agents.
- Knowledge ofGraphRAG and knowledge graphs.
- Experience withTemporal 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
About the Role
We are looking for aSenior AI & Agentic System Engineerto 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 ofLLM 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 includingplanner/executor/verifier, multi-agent, HITL, retry, and failure-recovery patterns.
- Integrate LLM providers such asOpenAI, Anthropic, and Gemini, including structured outputs, function calling, context management, and prompt optimization.
- BuildRAG and knowledge pipelinesusing embeddings, vector databases, hybrid search, reranking, and agent memory.
- Develop scalable backend services usingPython 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 asMCP, A2A, reasoning models, and agent frameworksand recommend practical adoption.
Required Skills
- 5+ years of software/backend engineering experience with strong system-design expertise.
- Hands-on experience buildingproduction AI agents, beyond basic chatbot implementations.
- Strong experience with at least one major LLM provider SDK:OpenAI, Anthropic, or Gemini.
- Strong understanding ofprompt engineering, tool/function calling, structured outputs, context management, and token optimization.
- Experience with agent frameworks/patterns such asLangGraph, CrewAI, AutoGen, ReAct, or plan-and-execute.
- StrongPython and/or Node.js/TypeScriptdevelopment skills.
- Experience withRAG, embeddings, vector databases, hybrid retrieval, and reranking.
- Strong knowledge ofREST/GraphQL APIs, asynchronous processing, databases, and distributed systems.
- Hands-on AWS experience, particularlyLambda, ECS/Fargate, API Gateway, S3, SQS, SNS/EventBridge, IAM, and CloudWatch.
- Experience withDocker, GitHub Actions, and Terraform.
- Understanding of AI security, includingprompt 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 usingPlaywright or Puppeteer.
- Experience with multimodal/vision-based agents.
- Knowledge ofGraphRAG and knowledge graphs.
- Experience withTemporal 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
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