Lead Machine Learning Engineer

Key Responsibilities

• Lead technical design, develop, and deploy machine learning models and systems with a focus on product integration
• Collaborate with product teams to translate business requirements into technical solutions
• Build and optimize ML pipelines for training, evaluation, and deployment
• Implement best practices for model monitoring, maintenance, and improvement
• Work with generative AI technologies to create novel product features
• Mentor junior team members on ML best practices and engineering principles

Required Qualifications

• 6+ years of experience building and deploying ML systems in production environments
• Strong foundation in machine learning algorithms, frameworks, and techniques
• Experience with at least one deep learning framework (PyTorch, TensorFlow, etc.)
• Proficiency in Python and related ML/data libraries (scikit-learn, pandas, numpy)
• Demonstrated experience integrating ML capabilities into user-facing products
• Understanding of ML operations, including model serving, monitoring, and maintenance
• Background in MLOps practices and tools (feature stores, experiment tracking, model registry)
• Experience with generative AI concepts, including large language models, diffusion models, or other generative architectures

Preferred Qualifications

• Experience with prompt engineering and fine-tuning large language models
• Knowledge of vector databases and retrieval-augmented generation
• Experience with cloud platforms (AWS, GCP, Azure) for ML workloads
• Familiarity with container orchestration technologies (Kubernetes, Docker)
• Track record of launching ML-powered products that drove measurable business impact

Technical Skills

Core Technologies

• Python
• PyTorch, Tensorflow
• Scikit-learn, pandas, numpy
• Azure Cloud
• Generative AI Frameworks
• API frameworks (FAST, Flask, etc.,)

Nice to Have

• AWS Cloud
• GCP Cloud
• Docker/Kubernetes
• CI/CD Pipelines