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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
Job Description:
1. Machine Learning Development & Deployment
· Design and implement supervised and unsupervised models for predictive analytics, including churn prediction, demand forecasting, renewal risk scoring, and cross sell/upsell opportunity identification.
· Translate business problems into ML frameworks and production solutions that improve efficiency, revenue, or customer experience.
· Build, optimize, and maintain ML pipelines using tools such as MLflow, Airflow, or Kubeflow.
2. Cross-Functional ML Use Cases
· Partner with teams across Sales (e.g., lead scoring, next-best action), Customer Service (e.g., case deflection, sentiment analysis), Finance (e.g., revenue forecasting, fraud detection), Supply Chain (e.g., inventory optimization, ETA prediction), and Order Fulfillment (e.g., delivery risk modeling) to define impactful ML use cases.
· Develop domain-specific models and continuously improve them using feedback loops and real-world performance data. 3.
3. Model Governance and MLOps
· Ensure robust model monitoring, versioning, and retraining strategies to keep models reliable in dynamic environments.
· Work closely with DevOps and Data Engineering teams to automate deployment, CI/CD workflows, and cloud-native ML infrastructure (AWS/GCP/Azure).
4. Data Engineering and Feature Architecture
· Collaborate with data engineers to define feature stores, data quality checks, and model-ready datasets on platforms like Snowflake or Databricks.
· Perform feature selection, transformation, and engineering aligned with each domain’s business logic. 5. Communication & Stakeholder Collaboration
· Present technical insights and model results to business and executive stakeholders in a clear, actionable format.
· Work with Product Owners and Program Managers to scope, prioritize, and plan delivery of ML projects.
Qualifications:
Required
• Bachelor’s or Master’s degree in (e.g., Computer Science, Engineering, Statistics, Mathematics)
• 4+ years of experience in machine learning, data science.
• Proficiency in Python, XGBoost, PyTorch, TensorFlow, or similar.
• Experience deploying models into production using ML pipelines and orchestration frameworks.
• Strong understanding of data structures, SQL, and cloud platforms (e.g., AWS SageMaker, Azure ML, or GCP Vertex AI).
• Hands-on experience in implementing machine learning algorithms such as Random Forest, XGBoost, Logistic Regression, and Deep Learning techniques including Neural Networks (ANN, CNN)
Preferred:
• Experience supporting business functions such as Finance, Sales, or Operations with ML use cases.
• Familiarity with MLOps tools (MLflow, SageMaker Pipelines, Feature Store).
• Exposure to enterprise data platforms (e.g., Snowflake, Oracle Fusion, Salesforce).
• Background in statistics, forecasting, optimization, or recommendation systems.
Data ScienceMachine Learning (ML)XressBees – a logistics company started in 2015 – is amongst the fastest growing companies of its sector. Our
vision to evolve into a strong full-service logistics organization reflects itself in the various lines of business like B2C
logistics 3PL, B2B Xpress, Hyperlocal and Cross border Logistics.
Our strong domain expertise and constant focus on innovation has helped us rapidly evolve as the most trusted
logistics partner of India. XB has progressively carved our way towards best-in-class technology platforms, an
extensive logistics network reach, and a seamless last mile management system.
While on this aggressive growth path, we seek to become the one-stop-shop for end-to-end logistics solutions. Our
big focus areas for the very near future include strengthening our presence as service providers of choice and
leveraging the power of technology to drive supply chain efficiencies.
Job Overview
XpressBees would enrich and scale its end-to-end logistics solutions at a high pace. This is a great opportunity to join
the team working on forming and delivering the operational strategy behind Artificial Intelligence / Machine Learning
and Data Engineering, leading projects and teams of AI Engineers collaborating with Data Scientists. In your role, you
will build high performance AI/ML solutions using groundbreaking AI/ML and BigData technologies. You will need to
understand business requirements and convert them to a solvable data science problem statement. You will be
involved in end to end AI/ML projects, starting from smaller scale POCs all the way to full scale ML pipelines in
production.
Seasoned AI/ML Engineers would own the implementation and productionzation of cutting-edge AI driven algorithmic
components for search, recommendation and insights to improve the efficiencies of the logistics supply chain and
serve the customer better.
You will apply innovative ML tools and concepts to deliver value to our teams and customers and make an impact to
the organization while solving challenging problems in the areas of AI, ML , Data Analytics and Computer Science.
Opportunities for application:
- Route Optimization
- Address / Geo-Coding Engine
- Anomaly detection, Computer Vision (e.g. loading / unloading)
- Fraud Detection (fake delivery attempts)
- Promise Recommendation Engine etc.
- Customer & Tech support solutions, e.g. chat bots.
- Breach detection / prediction
An Artificial Intelligence Engineer would apply himself/herself in the areas of -
- Deep Learning, NLP, Reinforcement Learning
- Machine Learning - Logistic Regression, Decision Trees, Random Forests, XGBoost, etc..
- Driving Optimization via LPs, MILPs, Stochastic Programs, and MDPs
- Operations Research, Supply Chain Optimization, and Data Analytics/Visualization
- Computer Vision and OCR technologies
The AI Engineering team enables internal teams to add AI capabilities to their Apps and Workflows easily via APIs
without needing to build AI expertise in each team – Decision Support, NLP, Computer Vision, for Public Clouds and
Enterprise in NLU, Vision and Conversational AI.Candidate is adept at working with large data sets to find
opportunities for product and process optimization and using models to test the effectiveness of different courses of
action. They must have knowledge using a variety of data mining/data analysis methods, using a variety of data tools,
building, and implementing models, using/creating algorithms, and creating/running simulations. They must be
comfortable working with a wide range of stakeholders and functional teams. The right candidate will have a passion
for discovering solutions hidden in large data sets and working with stakeholders to improve business outcomes.
Roles & Responsibilities
● Develop scalable infrastructure, including microservices and backend, that automates training and
deployment of ML models.
● Building cloud services in Decision Support (Anomaly Detection, Time series forecasting, Fraud detection,
Risk prevention, Predictive analytics), computer vision, natural language processing (NLP) and speech that
work out of the box.
● Brainstorm and Design various POCs using ML/DL/NLP solutions for new or existing enterprise problems.
● Work with fellow data scientists/SW engineers to build out other parts of the infrastructure, effectively
communicating your needs and understanding theirs and address external and internal shareholder's
product challenges.
● Build core of Artificial Intelligence and AI Services such as Decision Support, Vision, Speech, Text, NLP, NLU,
and others.
● Leverage Cloud technology –AWS, GCP, Azure
● Experiment with ML models in Python using machine learning libraries (Pytorch, Tensorflow), Big Data,
Hadoop, HBase, Spark, etc
● Work with stakeholders throughout the organization to identify opportunities for leveraging company data to
drive business solutions.
● Mine and analyze data from company databases to drive optimization and improvement of product
development, marketing techniques and business strategies.
● Assess the effectiveness and accuracy of new data sources and data gathering techniques.
● Develop custom data models and algorithms to apply to data sets.
● Use predictive modeling to increase and optimize customer experiences, supply chain metric and other
business outcomes.
● Develop company A/B testing framework and test model quality.
● Coordinate with different functional teams to implement models and monitor outcomes.
● Develop processes and tools to monitor and analyze model performance and data accuracy.
● Develop scalable infrastructure, including microservices and backend, that automates training and
deployment of ML models.
● Brainstorm and Design various POCs using ML/DL/NLP solutions for new or existing enterprise problems.
● Work with fellow data scientists/SW engineers to build out other parts of the infrastructure, effectively
communicating your needs and understanding theirs and address external and internal shareholder's
product challenges.
● Deliver machine learning and data science projects with data science techniques and associated libraries
such as AI/ ML or equivalent NLP (Natural Language Processing) packages. Such techniques include a good
to phenomenal understanding of statistical models, probabilistic algorithms, classification, clustering, deep
learning or related approaches as it applies to financial applications.
● The role will encourage you to learn a wide array of capabilities, toolsets and architectural patterns for
successful delivery.
What is required of you?
You will get an opportunity to build and operate a suite of massive scale, integrated data/ML platforms in a broadly
distributed, multi-tenant cloud environment.
● B.S., M.S., or Ph.D. in Computer Science, Computer Engineering
● Coding knowledge and experience with several languages: C, C++, Java,JavaScript, etc.
● Experience with building high-performance, resilient, scalable, and well-engineered systems
● Experience in CI/CD and development best practices, instrumentation, logging systems
● Experience using statistical computer languages (R, Python, SLQ, etc.) to manipulate data and draw insights
from large data sets.
● Experience working with and creating data architectures.
● Good understanding of various machine learning and natural language processing technologies, such as
classification, information retrieval, clustering, knowledge graph, semi-supervised learning and ranking.
● Knowledge and experience in statistical and data mining techniques: GLM/Regression, Random Forest,
Boosting, Trees, text mining, social network analysis, etc.
● Knowledge on using web services: Redshift, S3, Spark, Digital Ocean, etc.
● Knowledge on creating and using advanced machine learning algorithms and statistics: regression,
simulation, scenario analysis, modeling, clustering, decision trees, neural networks, etc.
● Knowledge on analyzing data from 3rd party providers: Google Analytics, Site Catalyst, Core metrics,
AdWords, Crimson Hexagon, Facebook Insights, etc.
● Knowledge on distributed data/computing tools: Map/Reduce, Hadoop, Hive, Spark, MySQL, Kafka etc.
● Knowledge on visualizing/presenting data for stakeholders using: Quicksight, Periscope, Business Objects,
D3, ggplot, Tableau etc.
● Knowledge of a variety of machine learning techniques (clustering, decision tree learning, artificial neural
networks, etc.) and their real-world advantages/drawbacks.
● Knowledge of advanced statistical techniques and concepts (regression, properties of distributions,
statistical tests, and proper usage, etc.) and experience with applications.
● Experience building data pipelines that prep data for Machine learning and complete feedback loops.
● Knowledge of Machine Learning lifecycle and experience working with data scientists
● Experience with Relational databases and NoSQL databases
● Experience with workflow scheduling / orchestration such as Airflow or Oozie
● Working knowledge of current techniques and approaches in machine learning and statistical or
mathematical models
● Strong Data Engineering & ETL skills to build scalable data pipelines. Exposure to data streaming stack (e.g.
Kafka)
● Relevant experience in fine tuning and optimizing ML (especially Deep Learning) models to bring down
serving latency.
● Exposure to ML model productionzation stack (e.g. MLFlow, Docker)
● Excellent exploratory data analysis skills to slice & dice data at scale using SQL in Redshift/BigQuery.
Data Science- 3-5yrs of practical DS experience working with varied data sets. Working with retail banking is preferred but not necessary.
- Need to be strong in concepts of statistical modelling – particularly looking for practical knowledge learnt from work experience (should be able to give "rule of thumb" answers)
- Strong problem solving skills and the ability to articulate really well.
- Ideally, the data scientist should have interfaced with data engineering and model deployment teams to bring models / solutions to "live" in production.
- Strong working knowledge of python ML stack is very important here.
- Willing to work on diverse range of tasks in building ML related capability on the Corridor Platform as well as client work.
- Someone with strong interest in data engineering aspect of ML is highly preferred, i.e. can play dual role of Data Scientist as well as someone who can code a module on our Corridor Platform writing robust code.
Structured ML techniques for candidates:
- GBM
- XgBoost
- Random Forest
- Neural Net
- Logistic Regression
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