AI Engineering Visionary

8 hours ago

Madurai, Tamil Nadu, India beBeeEngineering Full time ₹ 1,80,00,000 - ₹ 2,40,00,000
AI Engineering Leader

Drive the development and deployment of AI-powered workflows that transform personalized insights.

This role is ideal for someone passionate about applied AI and engineering with expertise in building, optimizing, and deploying AI systems that go beyond prototypes and into real-world, production-grade systems.

Key ResponsibilitiesDesign & Implementation
  • Design and implement Retrieval-Augmented Generation (RAG) pipelines for semantic search, personalization, and contextual enrichment.
  • Build agentic AI workflows using Pinecone, LangChain/LangGraph, and custom orchestration.
  • Integrate Large Language Model (LLM)-driven features into production systems, balancing innovation with scalability.
Vector Search & Data Intelligence
  • Architect and optimize vector databases (Pinecone, FAISS, Milvus) for low-latency retrieval.
  • Work with structured/unstructured datasets for embedding, indexing, and enrichment.
  • Collaborate with data engineers on Extract-Transform-Load/Extract-Load-Transform (ETL/ELT) pipelines to prepare data for AI applications.
Collaboration & Agile Delivery
  • Partner with backend and frontend engineers to integrate AI features into user-facing products.
  • Participate in Agile ceremonies (sprint planning, reviews, standups).
  • Maintain clear documentation and support knowledge sharing across the AI team.
Tech Stack
  • AI Tools: Pinecone, LangChain, LangGraph, OpenAI APIs (ChatGPT, GPT-4/5), HuggingFace models.
  • Languages: Python (primary for AI workflows), basic Node.js knowledge for integration.
  • Cloud & DevOps: AWS (Lambda, S3, RDS, DynamoDB, IAM), Docker, CI/CD pipelines.
  • Data Engineering: SQL, Python (Pandas, NumPy), ETL/ELT workflows, Databases (Postgres, DynamoDB, Redis).
  • Bonus Exposure: React, Next.js.
Required Qualifications
  • 5+ years in AI/ML engineering or software engineering with applied AI focus.
  • Hands-on experience with RAG pipelines, vector databases (Pinecone, FAISS, Milvus), and LLM integration.
  • Strong background in Python for AI workflows (embeddings, orchestration, optimization).
  • Familiarity with agentic architectures (LangChain, LangGraph, or similar).
  • Experience deploying and scaling AI features on AWS cloud environments.
  • Strong collaboration and communication skills for cross-functional teamwork.
Preferred Skills
  • Experience with embedding models, HuggingFace Transformers, or fine-tuning LLMs.
  • Knowledge of compliance frameworks (GDPR, HIPAA, SOC 2).
  • Exposure to personalization engines, recommender systems, or conversational AI.

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