Design, develop, and deploy production ML models; build feature pipelines; run experiments and A/B tests; monitor and maintain models; collaborate with engineers, product, and executives; mentor junior engineers and document models and processes.
We’re seeking a Senior-Level Machine Learning Engineer to join our growing Data Science & Engineering team. In this role, you will design, develop, and deploy ML models that power our cutting-edge technologies like voice ordering, prediction algorithms and customer-facing analytics. You’ll collaborate closely with data engineers, backend engineers, and product managers to take models from prototyping through to production, continuously improving accuracy, scalability, and maintainability.
Some subjective notes: This is our first Machine Learning hire. We are looking for a senior level engineer who can help guide us in this implementation. We are looking for someone with clear verbal and written communication skills, who can make their presence felt in meetings with C level executives. We are looking to learn from this person on what is the best method to implement machine learning across our organization, as we are looking at ML as a layer that will run across multiple products in our organization. This person will coordinate closely with the product person, other engineers and C level execs to shape the product and also drive implementation. To specify again, this is a senior level role but in an IC capacity and will grow into a role that will build a team under him / her.
Essential Job Functions- Model Development: Design and build next-generation ML models using advanced tools like PyTorch, Gemini, and Amazon SageMaker - primarily on Google Cloud or AWS platforms.
- Feature Engineering: Build robust feature pipelines; extract, clean, and transform large-scale transactional and behavioral data. Engineer features like time-based attributes, aggregated order metrics, categorical encodings (LabelEncoder, frequency encoding).
- Experimentation & Evaluation: Define metrics, run A/B tests, conduct cross-validation, and analyze model performance to guide iterative improvements. Train and tune regression models (XGBoost, LightGBM, scikit-learn, TensorFlow/Keras) to minimize MAE/RMSE and maximize R².
- Own the entire modeling lifecycle end-to-end, including feature creation, model development, testing, experimentation, monitoring, explainability, and model maintenance.
- Monitoring & Maintenance: Implement logging, monitoring, and alerting for model drift and data-quality issues; schedule retraining workflows.
- Collaboration & Mentorship: Collaborate closely with data science, engineering, and product teams to define, explore, and implement solutions to open-ended problems that advance the capabilities and applications of Checkmate, mentor junior engineers on best practices in ML engineering.
- Documentation & Communication: Produce clear documentation of model architecture, data schemas, and operational procedures; present findings to technical and non-technical stakeholders.
Requirements
- Academics: Bachelors/Master’s degree in Computer Science, Engineering, Statistics, or related field
- Experience: 5+ years of industry experience (or 1+ year post-PhD)
- Building and deploying advanced machine learning models that drive business impact
- Proven experience shipping production-grade ML models and optimization systems, including expertise in experimentation and evaluation techniques.
- Hands-on experience building and maintaining scalable backend systems and ML inference pipelines for real-time or batch prediction
- Programming & Tools:
- Proficient in Python and libraries such as pandas, NumPy, scikit-learn; familiarity with TensorFlow or PyTorch.
- Hands-on with at least one cloud ML platform (AWS SageMaker, Google Vertex AI, or Azure ML).
- Data Engineering:
- Hands-on experience with SQL and NoSQL databases; comfortable working with Spark or similar distributed frameworks.
- Strong foundation in statistics, probability, and ML algorithms like XGBoost/LightGBM; ability to interpret model outputs and optimize for business metrics.
- Experience with categorical encoding strategies and feature selection.
- Solid understanding of regression metrics (MAE, RMSE, R²) and hyperparameter tuning.
- Cloud & DevOps: Proven skills deploying ML solutions in AWS, GCP, or Azure; knowledge of Docker, Kubernetes, and CI/CD pipelines
- Collaboration: Excellent communication skills; ability to translate complex technical concepts into clear, actionable insights.
- Must be comfortable working in US hours at least till 5 pm EST.
- Master’s or advanced degree in Computer Science, Engineering, Statistics, or related field.
- Familiarity with data-privacy regulations (GDPR, CCPA) and best practices in secure ML.
- Open-source contributions or publications in ML/AI conferences.
- Experience with Ruby on Rails programming framework
Top Skills
Python,Pytorch,Gemini,Amazon Sagemaker,Google Cloud,Aws,Xgboost,Lightgbm,Scikit-Learn,Tensorflow,Keras,Pandas,Numpy,Sql,Nosql,Spark,Docker,Kubernetes,Ci/Cd,Google Vertex Ai,Azure Ml
Similar Jobs
Events • Analytics • Consulting
The Senior Machine Learning Engineer will design, build, and operate scalable ML systems for conversational AI, focusing on multiagent frameworks and answer quality monitoring.
Top Skills:
ArizeAWSConversational AiLangsmithMlopsPython
Artificial Intelligence • Consumer Web • Digital Media • Information Technology • Social Impact • Software
The role involves building a recommendation engine, designing ML systems, making data-driven decisions, and collaborating with leadership to enhance user experiences.
Top Skills:
Backend EngineeringData EngineeringMachine Learning
Artificial Intelligence • Big Data • Cloud • Information Technology • Machine Learning • Software
Lead design, development, and operation of production AI/ML systems (LLMs, RAG, multi-agent). Drive architectures, MLOps, model evaluation, cloud deployments, and mentor junior engineers to align AI with product needs.
Top Skills:
Python,Ml,Nlp,Llm,Rag,Embeddings,Retrieval Systems,Semantic Search,Mlops,Ci/Cd,Aws,Multi-Agent Systems,Reinforcement Learning,Experiment Tracking
What you need to know about the Mumbai Tech Scene
From haggling for the best price at Chor Bazaar to the bustle of Crawford Market, the energy of Mumbai's traditional markets is a key part of the city's charm. And while these markets will always have their place, the city also boasts a thriving e-commerce scene, ranking among the largest in the region. Driven by online sales in everything from snacks to licensed sports merchandise to children's apparel, the local industry is worth billions, with companies actively recruiting to meet the demands of continued growth.
