AI Data Specialist

About the role

As an AI Data Specialist, you will play a key role in the design, development, and deployment of an Agentic AI platform that streamlines the creation of AI-driven customer solutions. You will work closely with AI engineers and cross-functional teams to support the deployment, integration, and scaling of AI services across enterprise environments.

Responsibilities

• Design, develop, and implement data-driven AI solutions for an Agentic AI platform, aligning with enterprise architecture and business objectives.
• Build and maintain scalable data processing pipelines for AI workloads, including data ingestion, cleansing, transformation, and feature engineering.
• Develop and deploy end-to-end AI systems using LLMs, SLMs, and VLMs for advanced data processing, enrichment, and automation.
• Leverage NVIDIA AI technologies (e.g., CUDA, NV-Ingest, VLM..etc) or similar platforms to optimize model training, fine-tuning, and inference performance.
• Implement and manage vector databases such as Milvus, PostgreSQL (PGVector), or other vector stores to support semantic search, embeddings, and Retrieval-Augmented Generation (RAG) use cases.
• Design and optimize data and retrieval pipelines that integrate structured and unstructured data with LLM-based reasoning systems.
• Develop and integrate AI components (APIs, microservices, inference layers) into enterprise platforms across cloud, on-premise, and edge environments.
• Select and implement data engineering and pipeline orchestration tools to ensure scalable and reliable data workflows.
• Apply best practices in data engineering, MLOps, and AIOps, including pipeline monitoring, versioning, and performance tuning.
• Ensure data security, governance, and compliance, including encryption, access control, and secure data handling practices.
• Write clean, maintainable, and reusable code following enterprise development standards and best practices.
• Create detailed technical documentation, including data flow architectures, pipeline designs, model integration patterns, and system interfaces.
• Continuously evaluate and adopt emerging AI, LLM, and data technologies to improve solution effectiveness and scalability.
• Support solution design discussions, PoCs, and pre-sales activities by providing expertise in AI data architecture and implementation.
• Effectively communicate technical designs, data strategies, and AI solutions to both technical teams and business stakeholders.

Requirements

• Bachelor’s or Master’s degree in Computer Science, Data Engineering, Artificial Intelligence, or a related field.
• 3-5+ years of experience in AI/data engineering and implementation roles, including data engineering, machine learning, NLP, or Generative AI-focused data solutions.

Preferred Qualifications

• 2+ years of hands-on experience in building end-to-end Generative AI and data-driven solutions, including data preprocessing, embedding generation, and Retrieval-Augmented Generation (RAG) pipelines using LLMs and multimodal models (e.g., OpenAI, Anthropic, Llama, Hugging Face, Amazon Bedrock).
• Strong experience with data processing frameworks and pipeline development.
• Hands-on experience with vector databases such as Milvus, PostgreSQL (PGVector), Pinecone, or similar, including embedding management and semantic search implementations.
• Experience working with the NVIDIA AI ecosystem (e.g., GPUs, CUDA, TensorRT, NeMo) or equivalent acceleration technologies for efficient data processing, model training, and inference is a strong plus.
• Strong understanding of data architectures involving structured, semi-structured, and unstructured data, and building scalable pipelines to support AI/ML workloads.
• Experience integrating AI/data solutions across cloud and on-premise environments, including containerization and orchestration using Docker and Kubernetes.
• Proven experience implementing MLOps, LLMOps, and DataOps practices, including CI/CD pipelines, data versioning, monitoring, and lifecycle management using tools such as Jenkins, GitLab, MLflow, or similar.
• Solid understanding of data governance, security, and compliance, including handling sensitive data and implementing access controls and encryption.
• Strong problem-solving skills and ability to translate complex data and AI requirements into scalable, reusable solutions.