Group Description
We are seeking a hands-on AI Technical Consultant to support the design and implementation of production-grade AI and Generative AI systems, with a strong focus on deep technical engineering and data pipelines. This role centers on building and integrating AI solutions into scalable, cloud-based platforms under the guidance of senior architects and leads.
The ideal candidate has solid foundations in AI/ML engineering and data engineering, and is comfortable building, testing, and deploying AI components within enterprise environments. You will work closely with senior engineers and architects to deliver reliable and scalable AI solutions aligned with business needs. We expect 2–4 years of professional experience in AI/ML, data engineering, or advanced analytics engineering roles.
Responsibilities
Implement AI and Generative AI solution components under defined architecture and design guidance.
Build and maintain data pipelines and feature engineering workflows for AI use cases.
Support development and integration of LLM and Generative AI solutions into applications and platforms.
Develop model training, evaluation, and inference pipelines.
Assist in building scalable AI services and APIs.
Contribute to vector search, embeddings pipelines, and retrieval-based AI patterns (e.g., RAG).
Support MLOps / LLMOps practices including testing, deployment, and monitoring.
Troubleshoot performance, data quality, and model integration issues.
Collaborate with data engineers, ML engineers, and solution architects to deliver AI capabilities.
Contribute to technical documentation and implementation standards.
Key Skills
Hands-on experience with Python for AI/ML and data engineering workloads.
Working knowledge of ML and deep learning frameworks (e.g., PyTorch, TensorFlow, or similar).
Experience building data pipelines and processing workflows.
Exposure to Generative AI and LLM integration patterns.
Familiarity with embeddings, vector databases, or retrieval pipelines is a plus.
Experience integrating AI services via APIs.
Cloud platform exposure (Azure, AWS, or GCP AI services).
Understanding of model lifecycle, evaluation, and deployment basics.
Familiarity with containerization and CI/CD concepts is beneficial.
Strong problem-solving skills and willingness to work in fast-moving technical teams.