Senior Generative AI Developer

1 week ago

Gurugram, India Bean HR Consulting Full time

Job Title : Senior Generative AI Developer (4 to 7 years experience). Lead the design and deployment of enterprise-grade generative AI systems, driving innovation in LLM orchestration, multimodal architectures, and scalable AI/ML pipelines. Own the full lifecycle from research to production, ensuring alignment with business objectives and ethical AI standards. This will be a hands-on individual contributor role as well as providing technical guidance to junior 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 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 & 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 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 :Education : Bachelors/Masters in CS/AI or equivalent industry experience (5+ years in ML, 2+ in Mastery : : Expert-level PyTorch, TensorFlow Extended (TFX), ONNX : Certified in Azure AI Engineer Expert and/or GCP Professional ML 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 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 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, Qualifications Additions :Certifications :Azure : Microsoft Certified : Azure AI Engineer : Google Cloud Professional Machine Learning Engineer.- Experience with hybrid/multi-cloud GenAI deployments (e.g., training on GCP TPUs, serving via Azure endpoints). (ref:hirist.tech)

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