2492 - Lead Data Engineer

Position Summary:

We are seeking an accomplished and visionary Lead Data Engineer / Data Scientist with deep expertise in Generative AI (GenAI), MLOps, and AWS cloud infrastructure to join our cutting-edge AI/ML team. In this leadership role, you will drive the development of scalable data engineering frameworks, design and deploy advanced generative AI models, and establish robust MLOps pipelines to support enterprise-grade AI solutions.

The ideal candidate is a strategic thinker with hands-on experience in deploying AI systems at scale and a passion for building future-ready AI platforms. You will lead cross-functional initiatives, mentor junior engineers and data scientists, and shape the organization's AI capabilities by integrating emerging technologies and best practices.

Key Responsibilities:

Leadership & Strategy:

• Lead the design and implementation of scalable, production-ready AI and data engineering solutions aligned with business objectives.
• Collaborate with senior stakeholders to define technical roadmaps, model governance strategies, and long-term AI adoption plans.
• Mentor and guide a team of data engineers and data scientists, fostering a culture of innovation, collaboration, and continuous learning.

Data Engineering:

• Architect and oversee the development of large-scale, fault-tolerant data pipelines using AWS services (e.g., S3, Glue, Redshift, Kinesis) to support real-time and batch ML workflows.
• Ensure seamless data integration, transformation, and storage for GenAI model training and inference.
• Implement data quality frameworks, ensuring high data integrity and availability across the ML lifecycle.

Machine Learning & Generative AI:

• Design, train, and fine-tune Generative AI models such as Transformers, GANs, LLMs (e.g., GPT, BERT) for applications like content generation, personalization, and predictive analytics.
• Collaborate with data scientists to develop cutting-edge ML models using PyTorch, TensorFlow, and Hugging Face libraries.
• Champion the adoption of responsible AI practices, including explainability, fairness, and ethical deployment of GenAI models.

MLOps & Model Deployment:

• Lead the implementation of scalable MLOps pipelines, ensuring automated model deployment, monitoring, and lifecycle management using tools such as AWS SageMaker, MLflow, Kubeflow, or TFX.
• Develop CI/CD workflows tailored for ML using AWS CodePipeline, GitHub Actions, or similar tools.
• Monitor model performance post-deployment, establish alerting mechanisms, and iterate models based on real-time feedback and data drift.

Cloud Infrastructure & DevOps:

• Architect robust and cost-efficient AI/ML infrastructure in AWS, leveraging services like ECS, EKS, Lambda, and CloudFormation.
• Implement best practices in infrastructure as code (IaC) for consistent and scalable cloud deployments.
• Ensure data security, compliance, and governance when handling sensitive information and AI assets in the cloud.

Cross-functional Collaboration:

• Serve as a technical liaison between engineering, product, and business teams to translate complex AI concepts into practical business value.
• Present insights and progress to executive leadership, translating technical results into strategic recommendations.
• Champion collaborative problem-solving across departments, fostering innovation in AI applications.

Skills & Qualifications:

Technical Expertise:

• 8+ years of experience in Data Engineering, Data Science, or Machine Learning, with at least 2 years in a leadership role.
• Proven hands-on experience with AWS services (S3, EC2, SageMaker, Lambda, Redshift, ECS, EKS, Glue).
• Strong programming skills in Python, with experience using data manipulation libraries (Pandas, NumPy, Dask).
• Proficient in building and deploying GenAI models using frameworks like TensorFlow, PyTorch, Hugging Face Transformers.
• Experience with containerization (Docker) and orchestration (Kubernetes) for ML workloads.
• Familiarity with MLOps tools (MLflow, Kubeflow, TFX, Seldon) and version control systems (Git).

Leadership & Communication:

• Demonstrated success in leading AI/ML teams and initiatives in fast-paced, cloud-native environments.
• Ability to articulate complex technical concepts to non-technical stakeholders and senior executives.
• A proactive mindset with strong problem-solving skills and the ability to manage multiple priorities effectively.

Preferred Qualifications:

• Experience with GenAI applications in domains like content generation, synthetic data, simulations, or chatbots.
• Knowledge of data governance, responsible AI frameworks, and compliance standards (e.g., GDPR, HIPAA).
• Experience with Infrastructure as Code (Terraform, AWS CloudFormation).