ML / AI Data Engineer (Contract)

About us:

Working at Tech Holding isn't just a job, it's an opportunity to be a part of something bigger. We are a full-service consulting firm that was founded on the premise of delivering predictable outcomes and high-quality solutions to our clients. Our founders and team members have industry experience and have held senior positions in a wide variety of companies – from emerging startups to large Fortune 50 firms – and we have taken our combined experiences and developed a unique approach that is supported by the principles of deep expertise, integrity, transparency, and dependability.

Key Responsibilities

• Design, deploy, and scale large-scale ML and data processing pipelines across cloud infrastructure.
• Build systems to ingest, process, and serve 250,000+ hours of multimodal data (video, audio, metadata).
• Architect and optimize GPU-based compute environments (e.g., NVIDIA Tesla clusters) for distributed training and inference.
• Develop high-throughput backend systems for video ingestion from desktop and mobile platforms.
• Implement distributed processing workflows, including job scheduling, fault tolerance, and resource allocation.
• Design and build human-in-the-loop and automated annotation systems to ensure data quality and scalability.
• Translate ML and multimodal research into scalable, production-grade cloud architectures.
• Optimize pipelines for performance, reliability, and cost efficiency across compute, storage, and networking layers.
• Collaborate with ML, data, and engineering teams to deliver end-to-end data workflows.

Requirements

• 5+ years of experience in data engineering, ML pipelines, or distributed systems.
• Strong experience building scalable data pipelines for large datasets (video/audio preferred).
• Hands-on experience with cloud platforms (AWS, Azure, or GCP).
• Experience working with GPU-based environments and distributed computing.
• Strong programming skills in Python, Scala, or similar languages.
• Experience with data processing frameworks (Spark, Ray, Kafka, Airflow, or similar).
• Understanding of ML workflows, training pipelines, and inference systems.
• Experience designing fault-tolerant, high-availability systems.
• Strong knowledge of data storage systems (data lakes, object storage, distributed file systems).
• Ability to handle high-throughput, large-scale data ingestion and processing.

Good to Have

• Experience with multimodal AI (video, audio, NLP) systems.
• Familiarity with annotation tools and data labeling workflows.
• Experience with containerization and orchestration (Docker, Kubernetes).
• Knowledge of cost optimization strategies for large-scale cloud workloads.