Lead Data and AI Solution Engineer

Implement and deliver advanced data and AI solutions that drive innovation, optimize decision-making, and enhance operational efficiency—contributing directly to the organization's strategic growth and technological leadership.

Requirement Analysis & Solution Design

Collaborate with stakeholders to understand business needs, translate requirements into technical specifications, and architect tailored GenAI solutions (e.g., chatbots, content generators) that align with project goals.

Development & Deployment of GenAI Models Develop and deploy generative AI models (e.g., LLMs, GANs) on AWS.

Deploy scalable solutions into production environments, ensuring robustness and reliability.

Cross-functional Collaboration Partner with data scientists, software engineers, and business teams to integrate AI capabilities into workflows, ensuring alignment with technical and operational objectives.

Data Pipeline Management

Design and maintain efficient data pipelines for preprocessing, cleaning, and augmenting datasets.

Ensure data quality, governance, and compliance with privacy regulations System Integration & API Development Seamlessly integrate GenAI solutions into existing client infrastructure (e.g., cloud platforms, enterprise systems). Develop APIs and microservices to enable real-time AI functionality.

End-to-End GenAI Application Development Design, develop, and deploy user-facing applications powered by GenAI, including frontend interfaces (e.g., React) and backend services. Integrate AI models into applications to deliver seamless

AI/ML Engineering: 3–5 years of hands-on experience in designing, developing, and deploying machine learning/AI solutions, with 1–2 years focused on generative AI (e.g., LLMs, GANs, diffusion models).

Application Development: Proven track record in full-stack or backend development, including building and deploying AI-powered applications (e.g., chatbots, content automation tools).

Cloud Platforms: Experience with cloud services (AWS, Azure, GCP) for AI/ML workflows (e.g., SageMaker, Vertex AI, Azure ML) and infrastructure-as-code (Terraform, CloudFormation). Data Engineering: Expertise in building scalable data pipelines (ETL/ELT) using tools like Apache Spark, Airflow, or Databricks.

DevOps/MLOps: Familiarity with CI/CD pipelines, containerization (Docker, Kubernetes), and model monitoring tools (MLflow).