Python
Machine Learning (ML)Machine Learning Engineer (For client company)
Location: Bengaluru, India (Hybrid/Onsite)
Experience: 3–4 years
The Role
We are looking for a Machine Learning Engineer to build and productionize models that power fall detection, vitals monitoring, and predictive health insights from radar sensor data.
You will work closely with hardware, data engineering, backend, and product teams to improve model accuracy, reduce false alarms, and deploy reliable ML systems into production.
This role is ideal for someone with strong classical machine learning fundamentals who is comfortable working with messy real-world sensor data and writing clean, production-grade code.
What You'll Do
- Build and optimize classical ML models such as XGBoost, ensemble models, anomaly detection, and time-series models for fall detection, vitals monitoring, and health risk scoring.
- Engineer features from raw, sparse, and noisy radar signals, point-cloud data, and time-series sensor streams.
- Contribute to computer vision-adjacent problems such as pose estimation, movement analysis, skeleton tracking, and activity recognition using radar data.
- Build training, evaluation, and inference pipelines using Databricks.
- Perform exploratory data analysis on resident, device, alert, and facility-level datasets to identify trends, edge cases, and opportunities for model improvement.
- Define and own model evaluation metrics for safety-critical systems, including:
- Precision
- Recall
- Sensitivity
- Specificity
- False alarm rate
- Missed event rate
- Detection latency
- Analyze production model performance across facilities, residents, devices, and time periods.
- Handle noisy real-world datasets, including:
- Missing values
- Label quality issues
- Device variability
- Sparse event data
- Facility-specific patterns
- Write clean, modular, well-tested Python code for feature engineering, model training, evaluation, and inference.
- Deploy, monitor, and continuously improve production ML models.
- Collaborate with hardware and data engineering teams to improve data quality, labeling, observability, and model reliability.
What We're Looking For
- 3–4 years of experience building and deploying machine learning systems in production.
- Strong Python programming skills with the ability to write maintainable, testable, production-grade code.
- Strong understanding of classical machine learning concepts, including:
- Feature engineering
- Model training
- Cross-validation
- Error analysis
- Model evaluation
- Hands-on experience with algorithms such as:
- XGBoost
- Random Forests
- Gradient Boosting
- Ensemble methods
- Anomaly Detection
- Time-series models
- Strong SQL skills with experience analyzing large datasets using SQL, PySpark, Pandas, or Databricks.
- Experience working with time-series, sensor, spatial, point-cloud, IoT, or computer vision-style datasets.
- Familiarity with modern data engineering workflows using Databricks, Apache Spark, Delta Lake, or similar platforms.
- Strong debugging and analytical skills with the ability to diagnose issues across data pipelines, models, and production systems.
- Comfortable working in a fast-moving startup environment with ambiguity.
- Strong ownership mindset with the ability to take ML models from experimentation through production deployment.
Good to Have
- Experience in HealthTech, IoT, radar sensing, wearables, ambient monitoring, or safety-critical systems.
Exposure to:
- Computer Vision
- Pose Estimation
- Skeleton Tracking
- Object Tracking
- Spatial Data Processing
- Experience with:
- MLflow
- Model Registry
- Feature Stores
- Experiment Tracking
- Model Monitoring
- Experience with:
- ONNX
- Model Quantization
- Edge Deployment
- Latency Optimization
- Resource-Constrained Inference
- Familiarity with real-time data pipelines using:
- Kafka
- Spark Structured Streaming
- Streaming inference architectures
Shubham Vishwakarma
Full Stack Developer - Averlon
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Follow CutshortShubham Vishwakarma
Full Stack Developer - Averlon