Lead - GenAI Engineer

2 days ago

Bengaluru Delhi NCR Pune, India Bean Hr Consulting Full time ₹ 20,00,000 - ₹ 25,00,000 per year

Job Title : Data Science Lead/Lead GenAI Engineer

Location : Noida/ Gurugram/ Pune/ Bengaluru

Work mode : Hybrid

Contact :

Key Responsibilities

  1. Technical Leadership

  2. Architect multi-LLM systems (e.g., Mixture-of-Experts, LLM routing) for cost-performance optimization.

  3. Design GPU/TPU-optimized training pipelines (FSDP, DeepSpeed) for billion-parameter models.

  4. Cloud-Native AI Development

  5. Build multi-cloud GenAI platforms (Azure OpenAI + GCP Vertex AI + AWS Bedrock) with unified MLOps.

  6. Implement enterprise security: VPC peering, private model endpoints, and data residency compliance.

  7. Innovation & Strategy

  8. Pioneer GenAI use cases: Agentic workflows, AI-driven synthetic data generation, real-time fine-tuning.

  9. Establish AI governance frameworks: Model cards, drift monitoring, and red-teaming protocols.

  10. Cross-Functional Impact

  11. Partner with leadership to define AI roadmaps and ROI metrics (e.g., $ saved via AI-driven automation).

  12. Mentor junior engineers and evangelize GenAI best practices across the organization.

Qualifications

  • Education: Bachelors/Masters in CS/AI or equivalent industry experience (5+ years in ML, 2+ in GenAI).

  • Technical Mastery:

  • Languages: Python.

  • Frameworks: Expert-level PyTorch, TensorFlow Extended (TFX), ONNX Runtime.

  • Cloud: Certified in Azure AI Engineer Expert and/or GCP Professional ML Engineer.

  • GenAI Expertise:

  • Shipped production GenAI systems (e.g., 10k+ QPS chatbots, code autocomplete at GitHub Copilot scale).

  • Advanced prompt/response engineering: Self-critique chains, LLM cascades, guardrail-driven generation.

Must-Have Experience

  • Cloud AI experience:

  • Azure: Designed solutions with Azure OpenAI, MLOps Pipelines, and Cognitive Search.

  • GCP: Scaled Vertex AI LLM Evaluation, Gemini Multimodal, and TPU v5 Pods.

  • High-Impact Projects:

  • Automation projects to reduce significant $$ costs.

  • Built RAG systems with hybrid search (vector + lexical) and dynamic data hydration.
  • Led AI compliance for regulated industries (healthcare, finance).

Preferred Qualifications Additions

  • Certifications:

  • Azure: Microsoft Certified: Azure AI Engineer Associate.

  • GCP: Google Cloud Professional Machine Learning Engineer.

  • Experience with hybrid/multi-cloud GenAI deployments (e.g., training on GCP TPUs, serving via Azure endpoints).

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