AI/ML Engineer at Fire AI
Hyderabad, Telangana, India · noida · full time · mid level
Compensation: INR 1200000 per year
About the role
## About the role Fire AI is hiring three AI/ML Engineers in Hyderabad to design, train and deploy the production models behind its forecasting, anomaly detection and causal-chain analysis — end-to-end pipelines from data to inference, used by enterprises every day. Production ML engineering, not notebook research. **Openings:** 3 · **Experience:** 1+ years of production ML · **Employment:** full-time **Location:** Hyderabad, Telangana, India — new office in Financial District / HITEC City · **Working week:** 6 days — Monday to Friday from the office, Saturday from home · **Hours:** 10 AM – 7 PM · **Until the office opens (around 15 December):** work from home ## About Fire AI Fire AI is building a decision intelligence platform — not another BI tool. Intelligent dashboards, automated reporting, smart alerts in 60 seconds instead of 6 days, time-series forecasting at 94% accuracy, text-to-SQL at 98% accuracy and causal-chain analysis that answers "why", not just "what". 72% of its users are non-technical (CFOs, ops managers) working through conversational AI in 90+ languages. It serves 120+ brands across 17+ industries — including IRCTC, CWC, Faballey and GSN Group — with zero churn, grew from $28.3K to $512.9K MRR in seven months, and is backed by Venture Catalysts, IPV and SucSEED. The engineering team is expanding into a new Hyderabad office. ## What you will build - Design, train and deploy production ML models that meet accuracy and performance targets — end-to-end pipelines from data ingestion to inference serving, with model versioning, automated retraining and continuous learning. - Build scalable ML infrastructure: feature-engineering pipelines, MLOps workflows for monitoring, drift detection and performance tracking, and A/B testing frameworks for model evaluation. - Deploy models with monitoring and alerting; keep accuracy, latency and availability SLAs; implement explainability and bias detection; optimise inference cost. - Integrate models with backend APIs, frontend UIs and customer dashboards, working with Senior AI Engineers on agentic systems, RAG pipelines and LLM orchestration. - Optimise for scale — compress models, reduce inference latency and keep them reliable across 120+ brands. ## What you need - Has deployed ML models to production — not only trained them — with an end-to-end pipeline from data to inference - Python with PyTorch or TensorFlow, and Scikit-learn - MLOps tooling: MLflow or Kubeflow, Docker, and a cloud ML platform (AWS SageMaker, GCP or Azure) - Data engineering: Pandas, SQL, ETL pipelines, feature engineering - Production mindset: monitoring, A/B testing, automated retraining, inference speed and memory ## Nice to have - Time-series forecasting, anomaly detection or causal inference in production. - Feature stores and real-time data processing. - Exposure to RAG pipelines, LLM orchestration or agentic systems. **Interview process:** 2 rounds — with the Senior Solutions Architect and the Director of AI. **Career path:** AI/ML Engineer → Senior AI/ML Engineer → AI Architect → Director of AI Engineering. ## Working model - **Location:** Hyderabad, Telangana. Fire AI is opening a new office in Financial District / HITEC City. - **Six-day working week:** Monday to Friday from the office, Saturday from home. - **Hours:** 10 AM – 7 PM. - **Before the office opens:** the office is expected to be ready around 15 December; until then the team works from home. - **Not a hybrid role.** After the office opens, Monday to Friday in the office is required. - **Relocation:** candidates outside Hyderabad need to relocate.
Responsibilities
- Design, train and deploy production ML models that meet accuracy and performance targets — end-to-end pipelines from data ingestion to inference serving, with model versioning, automated retraining and continuous learning.
- Build scalable ML infrastructure: feature-engineering pipelines, MLOps workflows for monitoring, drift detection and performance tracking, and A/B testing frameworks for model evaluation.
- Deploy models with monitoring and alerting; keep accuracy, latency and availability SLAs; implement explainability and bias detection; optimise inference cost.
- Integrate models with backend APIs, frontend UIs and customer dashboards, working with Senior AI Engineers on agentic systems, RAG pipelines and LLM orchestration.
- Optimise for scale — compress models, reduce inference latency and keep them reliable across 120+ brands.
Requirements
- Has deployed ML models to production — not only trained them — with an end-to-end pipeline from data to inference
- Python with PyTorch or TensorFlow, and Scikit-learn
- MLOps tooling: MLflow or Kubeflow, Docker, and a cloud ML platform (AWS SageMaker, GCP or Azure)
- Data engineering: Pandas, SQL, ETL pipelines, feature engineering
- Production mindset: monitoring, A/B testing, automated retraining, inference speed and memory
Skills: Python, PyTorch, TensorFlow, Scikit-learn, MLflow, Kubeflow, Docker, AWS SageMaker, MLOps, Time-series forecasting, Anomaly detection, SQL, Feature engineering
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