Experience: 2-5 Years
Location: Baner., Pune
Role Overview
We're hiring an AI/ML Engineer to build intelligent, agent-driven capabilities into Apptimus — spanning ML models for prediction/anomaly detection and agentic workflows for autonomous reasoning and task execution across our IoT and facility data.
Responsibilities
- Design and build agentic AI workflows (LangChain/LangGraph/CrewAI) for multi-step reasoning, tool-calling, and automated decision-making on Apptimus data
- Develop and deploy ML models (forecasting, anomaly detection, classification) on IoT sensor and facility usage data, and wire them into agent pipelines as callable tools
- Write production Python for agent orchestration, model serving, and backend/API integration
- Write SQL for data extraction, feature engineering, RAG grounding, and reporting
- Implement prompt engineering, memory management, and guardrails to keep agents reliable in production
- Build and maintain embeddings/vector search pipelines to ground agents in Apptimus data (occupancy, energy, checklist/license usage, etc.)
- Evaluate agent and model performance (accuracy, latency, cost) and iterate through structured testing
Requirements
- 2+ years in AI/ML or software engineering, with hands-on exposure to LLM-based or agentic systems
- Strong Python — ML libraries (scikit-learn, pandas, PyTorch/TensorFlow) plus LLM SDKs (OpenAI, Anthropic)
- Good SQL — complex queries, joins, relational DBs
- Solid understanding of LLM concepts: prompting, embeddings, vector DBs, RAG
- Practical experience with at least one agent orchestration framework (LangChain, LangGraph, CrewAI, AutoGen)
- Fundamentals of ML model development and evaluation (regression/classification, time-series, or anomaly detection)
Nice to Have
- Hands-on experience shipping a production-ready agentic model/system — i.e., an agent that went beyond a prototype into a live, monitored deployment handling real users/data
- IoT/smart building or sensor data experience
- MCP or similar tool-calling protocols
- Docker, CI/CD, LLM observability tools (LangSmith, Langfuse)
- Experience combining classical ML models with LLM agents (model-as-tool patterns)
Pay: ₹1,000,000.00 - ₹1,500,000.00 per year
Work Location: In person
