Lead Machine Learning Engineer - Generative AI

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