Chief Data Architect

12 hours ago

Jodhpur, Rajasthan, India beBeeData Full time ₹ 1,50,00,000 - ₹ 2,50,00,000

Seeking a visionary Data Engineer to spearhead the design and development of scalable data pipelines. Collaborate with cross-functional teams to drive cutting-edge solutions, leveraging expertise in AI/ML integration and cloud infrastructure.

Key Responsibilities
  • Pipeline Development: Design high-throughput, low-latency pipelines to process massive datasets efficiently.
  • Data Ingestion & Transformation: Optimize data ingestion and transformation for seamless integration with AI/ML systems.
  • Model Pipelines: Leverage GenAI models under Vertex AI, fine-tune custom models when necessary, and design multi-layered model pipelines balancing accuracy, cost, and performance.
  • Scalable Solutions: Architect scalable solutions primarily on GCP (Vertex AI, BigQuery, GKE, Cloud Run, PostgreSQL, Dataflow, Pub/Sub), with Azure experience being a plus.
  • Performance Optimization: Ensure real-time, low-latency processing for high-volume workloads, optimize GPU/CPU-intensive environments, and manage cost and efficiency.
  • Collaboration: Work closely with data scientists, ML engineers, and solution architects, taking end-to-end ownership of assigned tasks and deliverables.
Required Skills & Qualifications
  • Programming Skills: Expert-level Python programming skills.
  • Streaming Pipelines: Hands-on experience with streaming pipelines, large-scale data ingestion frameworks, and AI/ML integration.
  • AI/ML Integration: Experience integrating and fine-tuning LLMs and GenAI models, comfortable working with custom model development when necessary.
  • Platform Experience: Familiarity with OpenAI, Gemini, Vertex AI, or equivalent platforms.
  • CLOUD Infrastructure: GCP experience is mandatory, with Azure experience being a plus.
  • Distributed Systems: Proven ability to design and implement high-performance, distributed data solutions.
  • Massive Dataset Processing: Experience processing massive datasets efficiently within strict timeframes.

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