Lead Machine Learning Engineer - AI startup
Founding Teams
Bengaluru, Karnataka, IndiaLEAD
Machine LearningAILeadership
Job Description
Lead Machine Learning Engineer at Founding Teams with a focus on AI startup development.
Responsibilities
- Lead the end-to-end development of machine learning models, from prototyping to production deployment.
- Architect scalable ML pipelines and infrastructure.
- Work closely with data scientists to transition research models into robust production systems.
- Collaborate with engineering teams to integrate ML models into applications and services.
- Manage and mentor a team of machine learning and data engineers.
- Establish best practices for model development, evaluation, monitoring, and retraining.
- Design experiments, analyze results, and iterate rapidly to improve model performance.
- Stay current with the latest research and developments in machine learning and AI.
- Define and enforce ML model governance, versioning, and documentation standards.
Qualifications
- Bachelor's or Master’s degree in Computer Science, Machine Learning, Data Science, Statistics, or a related field (PhD preferred but not required).
- 5+ years of professional experience in machine learning engineering.
- 2+ years of leadership or technical mentoring experience.
- Strong expertise in Python for machine learning (Pandas, NumPy, scikit-learn, etc.).
- Experience with deep learning frameworks such as TensorFlow, PyTorch, or JAX.
- Strong understanding of machine learning algorithms (supervised, unsupervised, reinforcement learning).
- Experience building and maintaining ML pipelines and data pipelines.
- Proficiency in model deployment techniques (e.g., serving models with REST APIs, gRPC, or via cloud services).
- Hands-on experience with cloud platforms (AWS, GCP, Azure) for model training and deployment.
- Deep understanding of MLOps concepts: monitoring, logging, CI/CD for ML, reproducibility.
- Experience with Docker and container orchestration (e.g., Kubernetes).
- Experience with feature stores (e.g., Feast, Tecton).
- Knowledge of distributed training (e.g., Horovod, distributed PyTorch).
- Familiarity with big data tools (e.g., Spark, Hadoop, Beam).
- Understanding of NLP, computer vision, or time series analysis techniques.
- Knowledge of experiment tracking tools (e.g., MLflow, Weights & Biases).
- Experience with model explainability techniques (e.g., SHAP, LIME).
- Familiarity with reinforcement learning or generative AI models.
- Languages: Python, SQL (optionally: Scala, Java for large-scale systems)
- ML Frameworks: TensorFlow, PyTorch, scikit-learn, XGBoost, LightGBM
- MLOps: MLflow, Weights & Biases, Kubeflow, Seldon Core
- Data Processing: Pandas, NumPy, Apache Spark, Beam
- Model Serving: TensorFlow Serving, TorchServe, FastAPI, Flask
- Cloud Platforms: AWS (SageMaker, S3, EC2), Google Cloud AI Platform, Azure ML
- Orchestration: Docker, Kubernetes, Airflow
- Databases: PostgreSQL, BigQuery, MongoDB, Redis
- Experiment Tracking & Monitoring: MLflow, Neptune.ai, Weights & Biases</