Website:
altaneo.in
Job details:
Company Description
Altaneo Tech specializes in empowering businesses with advanced web, app, AI solutions, and tailored digital marketing services to fuel innovation and growth. With expertise in custom software development, AI-driven automation, scalable applications, and data-focused strategies, we serve startups, SMEs, and enterprises. Our solutions are designed to boost efficiency, enhance customer engagement, and improve overall business performance. Driven by a team of skilled professionals and the latest technologies, we pride ourselves on delivering impactful and competitive solutions.
Role Description
Altaneo is hiring a full-time Artificial Intelligence Engineer for our on-site location in Gurugram. The role involves designing, developing, and optimizing AI models, performing tasks related to natural language processing, neural networks, and pattern recognition. You will collaborate with cross-functional teams to integrate AI solutions into software applications and participate in testing and enhancing system performance. An essential aspect of the role also includes researching emerging AI methodologies and applying them to real-world business challenges.
What You Will Do
Technical Architecture — You Own This Completely
•Design the multi-agent orchestration layer: choose and implement the right framework (LangChain, LangGraph, AutoGen, or CrewAI) for PropOS's specific agent coordination requirements. This is the most critical architectural decision of the company's first year.
•Build the WhatsApp-first delivery layer: integrate Meta WhatsApp Business API via a BSP partner (Gupshup or Kaleyra), handle multi-turn conversation state, manage session windows, implement media handling, and build robust fallback flows for API failures.
•Design the shared data ontology: model the PropOS unified data layer: Land Parcel → Approvals → Units → Bookings → Collections → Handover. This schema determines whether cross-agent intelligence is possible — get it wrong and everything is siloed.
•Own the LLM strategy: evaluate foundation models (GPT-4o, Claude, Sarvam AI, Krutrim) against cost, latency, and Hindi language quality benchmarks. Make the final call on which models power which agents.
•Build document ingestion pipelines: parse RERA approval letters, BOQ Excel files, CP agreements, and project drawings using pypdf, camelot, Textract, and custom parsers.
AI Agent Engineering — The Core Daily Work
•Implement production LLM agents: with tool use, memory management, multi-step reasoning, and human-in-the-loop decision points. Agents must handle ambiguous real estate conversations, not just structured commands.
•Build vernacular NLP capabilities: implement Hindi, Hinglish, and eventually Marathi/Gujarati intent classification. Evaluate Sarvam AI and IndicBERT for vernacular understanding. Fine-tune on real estate domain vocabulary.
•Design and maintain the agent evaluation framework: define success metrics for each agent (task completion rate, hallucination rate, fallback frequency), build automated test suites, and run weekly quality reviews.
•Set up LLM observability: implement LangSmith or equivalent for prompt tracking, latency monitoring, cost per conversation, and failure analysis. You will look at these dashboards every morning.
- Implement RAG pipelines: for developer project document search using Pinecone or Weaviate. Semantic chunking, re-ranking, and retrieval quality evaluation are your responsibility.
#artificialintelligence #ai #ml #NLP #LLM
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