DGM-Data Scientist

About the Role

Key responsibilities include developing generative AI models for text, image, and multimodal tasks using tools such as LangChain and LangGraph, building robust data pipelines for SQL/NoSQL sources, and designing end‑to‑end ML pipelines covering data ingestion, training, validation, deployment, and monitoring. The candidate will also implement feature engineering, model evaluation with observability tools, and optimize models for cost‑effective inference on AWS services like SageMaker, Lambda, and EKS/ECS. Additional duties involve automating DevOps/MLOps workflows, monitoring model drift, staying current with AI research, and collaborating closely with stakeholders to deliver AI‑driven features. • Design, develop, and fine‑tune generative AI models (LLMs, diffusion models, GANs) for text, image, and multimodal applications. • Build and maintain data pipelines for structured data sources (SQL/NoSQL) ensuring quality and consistency. • Create end‑to‑end ML pipelines with CI/CD, experiment tracking, model versioning, and rollback capabilities. • Implement Retrieval‑Augmented Generation, agentic AI, and knowledge augmentation using LangChain and LangGraph. • Deploy AI models on AWS (SageMaker, Lambda, ECS/EKS) with high availability, performance, and cost‑efficiency. • Optimize inference through model compression and monitor performance with LangSmith and MLflow. • Develop automated MLOps workflows using Kubeflow, Airflow, and Git CI/CD tools. • Detect and address model, data, or concept drift to trigger timely model retraining. • Conduct prompt engineering and fine‑tuning of large language models. • Collaborate with stakeholders and communicate AI‑driven solutions effectively.

Key Responsibilities

• ▸generative ai
• ▸data pipelines
• ▸mlops automation
• ▸aws deployment
• ▸model monitoring
• ▸prompt engineering

What You Bring

We are seeking a highly skilled Data Scientist to design, develop, and deploy advanced AI solutions leveraging cutting‑edge Generative AI technologies. The role requires hands‑on experience building Generative AI solutions with Large Language Models, Retrieval‑Augmented Generation, and Agentic AI, as well as expertise in querying and transforming structured datasets. Proficiency in AWS cloud deployment is essential to ensure scalable and secure enterprise applications. Required skills include strong proficiency in Python, LangChain, LangGraph, SciKit‑Learn, NumPy, and Pandas, as well as deep knowledge of generative AI techniques (LLMs, transformers, diffusion models, GANs, RAG, Agentic AI). Hands‑on experience with AWS services (SageMaker, EC2, S3, Lambda, EKS/ECS, API Gateway, Bedrock) and MLOps frameworks (Kubeflow, MLflow, Airflow) is mandatory. The candidate must also be familiar with API development, micro‑services architecture, and data security and cloud best practices, with an emphasis on cost optimisation. Preferred qualifications include experience with prompt engineering, fine‑tuning LLMs, distributed training, GPU optimisation, and a record of publications or contributions to AI research communities. A bachelor’s or master’s degree in Computer Science, Data Science, AI/ML, or a related field is required.

Requirements

• ▸python
• ▸aws
• ▸llms
• ▸mlops
• ▸prompt engineering
• ▸master's

Work Environment

Office Full-Time