We are seeking an Applied AI Engineer to build, evaluate, and deploy production AI applications using large language models, AI agents, and Retrieval-Augmented Generation (RAG). The role requires solid software engineering skills and hands-on experience translating business requirements into reliable AI workflows. You will collaborate with product and engineering teams to deliver measurable business outcomes.

Responsibilities

- Design and implement AI agent workflows using LangGraph, including nodes, edges, conditional routing, and state management.

- Build RAG pipelines covering ingestion, chunking, embeddings, vector indexing, hybrid search, reranking, and answer generation.

- Implement input validation, intent detection, query classification, query rewriting, history-aware retrieval, and semantic caching.

- Integrate LLMs with APIs, databases, tools, and enterprise data sources.

- Manage conversation history, agent memory, persisted state, and recovery from interrupted workflows.

- Create evaluation datasets and measure retrieval quality, answer quality, and agent task completion.

- Use Langfuse or equivalent tools to trace LLM calls, monitor latency and token costs, and investigate failures.

- Diagnose production issues such as incorrect retrieval, hallucinations, tool failures, and state inconsistencies.

- Handle API rate limits, timeouts, and transient failures through appropriate retries, backoff, concurrency controls, and fallbacks.

- Collaborate with product and engineering teams to deliver measurable business outcomes.

Requirements

- Proficiency in Python or TypeScript/JavaScript.

- Experience building backend services, REST APIs, and asynchronous workflows.

- Practical knowledge of testing, debugging, logging, Git,



and deployment.

- Ability to explain implementation choices and tradeoffs in previously delivered projects.

- Hands-on experience building agent workflows with LangGraph.

- Understanding of graph state, nodes, edges, conditional routing, and workflow termination.

- Experience with tool calling, structured outputs, state persistence, and error recovery.

- Understanding of the distinction between conversation history, agent memory, and caching.

- Experience implementing and debugging end-to-end RAG pipelines.

- Understanding of embeddings, vector dimensions, cosine similarity, and similarity search.

- Familiarity with a vector database or search engine such as pgvector, Qdrant, Pinecone, Weaviate, or Elasticsearch.

- Ability to select and justify chunking strategies, chunk sizes, and overlap.

- Experience with metadata filtering, keyword and semantic retrieval, hybrid search, and reranking.

- Ability to build representative evaluation datasets with expected answers or relevance labels.

- Understanding of retrieval metrics such as Precision@K, Recall@K, MRR, and nDCG.

- Experience evaluating answer correctness, relevance, groundedness, and citation accuracy.

- Ability to evaluate agent task success, tool-selection accuracy, reliability, latency, and cost.

- Familiarity with human review, LLM-based evaluation, and regression testing.

- Experience with Langfuse or comparable tracing and monitoring tools.

- Ability to trace failures across retrieval, prompts, model responses, and tool execution.

- Understanding of rate-limit handling, exponential backoff with jitter, retry limits, and request concurrency.

- Awareness of prompt injection, sensitive-data handling, and access control in AI applications.

📌 Applied AI Engineer (Chennai Metropolitan Area)
🏢 9th Direction Technology Solutions
📍 Chennai Metropolitan Area