Lead - GenAI Engineer

Job Title

Location :

Work mode

Contact

Key Responsibilities

1. Technical Leadership

• Architect

  multi-LLM systems

  (e.g., Mixture-of-Experts, LLM routing) for cost-performance optimization.
• Design

  GPU/TPU-optimized training pipelines

  (FSDP, DeepSpeed) for billion-parameter models.

2. Cloud-Native AI Development

• Build

  multi-cloud GenAI platforms

  (Azure OpenAI + GCP Vertex AI + AWS Bedrock) with unified MLOps.
• Implement

  enterprise security

  : VPC peering, private model endpoints, and data residency compliance.

3. Innovation & Strategy

• Pioneer

  GenAI use cases

  : Agentic workflows, AI-driven synthetic data generation, real-time fine-tuning.
• Establish

  AI governance frameworks

  : Model cards, drift monitoring, and red-teaming protocols.

4. Cross-Functional Impact

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