Job description
Description
We’re looking for a Senior Generative AI Developer to lead the design and deployment of enterprise-grade GenAI systems. This is a hands-on individual contributor role with opportunities to provide technical guidance to junior developers. You’ll drive innovation in LLM orchestration, multimodal architectures, and scalable AI/ML pipelines—owning the full lifecycle from research to production while ensuring alignment with business goals and ethical AI standards.
Requirements
Key Responsibilities:
- 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-grade security: VPC peering, private endpoints, data residency compliance
3. Innovation & Strategy
Pioneer GenAI use cases: agentic workflows, synthetic data generation, real-time fine-tuning
Establish AI governance frameworks: model cards, drift monitoring, red-teaming protocols
4. Cross-Functional Impact
Collaborate with leadership to define AI roadmaps and ROI metrics (e.g., cost savings via automation)
Mentor junior engineers and promote GenAI best practices across the organization
Qualifications:
Education:
Bachelor’s or Master’s in Computer Science, AI, or equivalent 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:
Delivered production-grade GenAI systems (e.g., 10k+ QPS chatbots, GitHub Copilot-scale autocomplete)
Advanced prompt engineering: self-critique chains, LLM cascades, guardrail-driven generation
Must-Have Experience:
Cloud AI Development:
Azure: Azure OpenAI, MLOps Pipelines, Cognitive Search
GCP: Vertex AI LLM Evaluation, Gemini Multimodal, TPU v5 Pods
High-Impact Projects:
Automation initiatives with measurable cost savings
RAG systems with hybrid search (vector + lexical) and dynamic data hydration
AI compliance leadership in regulated industries (e.g., healthcare, finance)
Preferred Qualifications:
Certifications:
Microsoft Certified: Azure AI Engineer Associate
Google Cloud Professional Machine Learning Engineer
Deployment Experience:
- Hybrid/multi-cloud GenAI setups (e.g., training on GCP TPUs, serving via Azure endpoints)
