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We are looking for Lead AI Engineer for a GCC
Experience :- 7.9 -10 years ( Do not apply if experience range is lower thn this)
Location :- Bangalore/Hybrid
Notice Period (immediate to 30 days notice period candidates only)
Core Responsibilities
• Own end-to-end technical design and delivery of GenAI/agentic systems for internal or external applications/
• Architect multi-agent workflows using tools like LangChain, A2A protocols, and custom orchestration frameworks/
• Guide the design and tuning of prompt architectures, context strategies (e.g., with MCP), and hybrid RAG pipelines.
• Integrate AI services into enterprise platforms such as Azure Foundry, Databricks, and core business systems.
• Lead engineering pods, mentor engineers across levels, and drive technical alignment across product and platform teams.
• Push the boundaries of performance, latency, and accuracy through research-backed optimization
• Define reusable templates, shared components, and internal GenAI SDKs.
• Enforce standards around ethical AI use, context control, prompt security, and hallucination mitigation.
Required Skills:
• 8+ years of experience in AI/ML/GenAI solutioning, with 3+ years in technical leadership.
• Deep proficiency in Python 3 with strong command over openai, pydantic, transformers, faiss, and langchain.
• Demonstrated experience in deploying scalable GenAI solutions with cloud-native design.
• Strong working knowledge of Azure cloud services, GitHub workflows, and CI/CD best practices.
• Experience in vector store optimization, token-level control, and prompt performance management
Job ID: 148087233
Skills:
containerization , agent coordination, Azure ecosystem, cloud-native GenAI solutions, agent-based workflows, LLM APIs, vector databases, CI CD, embedding generation, Python 3.11, model monitoring
Skills:
snowflake , Sql, Git, Gcp, Docker, FastAPI, Azure, Python, AWS, LangChain, CrewAI, LLMs, Semantic Kernel, LangGraph, Dataiku, LlamaIndex
Skills:
Jax, Cuda, Pytorch, Python, DDPM, DeepSpeed, BLIP-2, TensorRT, flamingo, Ray, controlnet, CLIP, multimodal transformers, PyTorch Lightning, VLMs, ONNX Runtime, LDM, deep-learning, LLaVA, diffusion models
Skills:
Machine Learning, Tensorflow, Pytorch, Docker, Python, AWS, Java, Natural Language Processing, Scala, Deep Learning, MLops, Gcp, Azure, Kubernetes, Computer Vision, Hugging Face, Go, Pinecone, Retrieval-Augmented Generation, Vector Databases, Large Language Models, Generative AI, FAISS, Transformers
Skills:
Docker, MLops, Etl, AWS, Python, Kubernetes, Azure, Gcp, Nlp, LLMs, Transformers, feature store, vector DBs, embeddings, data pipelines, RAG, prompt engineering
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