ML Lead Engineer

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 Skills"

 - 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. "Desirable Skills" - 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. "Education Qualification" - Bachelor's degree in Computer Science, Engineering, Mathematics, or related field. Technical Skills - Core Technologies: Python, PyTorch, Tensorflow, Scikit-learn, pandas, numpy, Azure Cloud, Generative AI Frameworks, API frameworks (FAST, Flask, etc.)