Senior Generative AI Developer

1 day 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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