Job Description
Job Title: AI Engineer
Location- Pan India
Experience- 4 to 8 years
Job Description-
Key Responsibilities:
• Design and implement AI solutions using Gen AI, Agentic AI, and LLMs.
• Build and orchestrate intelligent agents using Lang Chain, Lang Graph, and Model Context Protocol (MCP).
• Develop agent-to-agent communication using Agent to Agent Protocols.
• Apply Prompt Engineering, Distillation, and Fine-tuning techniques to optimize model performance.
• Integrate RAG (Retrieval-Augmented Generation) pipelines with Vector DBs and Embeddings.
• Ensure ethical AI development by applying Guard Rails, Responsible AI principles, and staying compliant with AI Ethics and Regulations.
• Deploy AI models using Docker, Kubernetes and other Gen AI deployment tools.
• Collaborate with cross-functional teams to deliver scalable and secure AI solutions.
• Stay updated with the latest advancements in multi-modal AI, LLMs, and cloud-native AI platforms.
Experience with any of the following Cloud Native AI Services:
• Azure: Azure AI Foundry, Azure AI Agents, Azure AI Search, Azure Open AI, Document Intelligence,
• AWS: Bedrock, Amazon Q, Amazon Sage Maker
• Google Cloud Platform (GCP): Vertex AI, Model Garden, Agentspace, Cloud Run, GKE, Agent Engine , Compute Engine
• Experience with CI/CD pipelines (Azure Dev Ops, Git Lab CI, Jenkins), infrastructure as code (Terraform, Cloud Formation, Azure Bicep), and monitoring/logging tools.
Qualifications:
• Bachelor’s / master’s degree in engineering or technology
• Proven experience in building and deploying AI/ML solutions in production.
• Strong understanding of AI model lifecycle, MLOps, and cloud-native architectures.
• Excellent problem-solving, communication, and collaboration skills.
Certifications: Azure AI Engineer, AWS Certified Machine Learning, Google Cloud Professional ML Engineer, and relevant AI/ML certifications.
Location- Pan India
Experience- 4 to 8 years
Job Description-
Key Responsibilities:
• Design and implement AI solutions using Gen AI, Agentic AI, and LLMs.
• Build and orchestrate intelligent agents using Lang Chain, Lang Graph, and Model Context Protocol (MCP).
• Develop agent-to-agent communication using Agent to Agent Protocols.
• Apply Prompt Engineering, Distillation, and Fine-tuning techniques to optimize model performance.
• Integrate RAG (Retrieval-Augmented Generation) pipelines with Vector DBs and Embeddings.
• Ensure ethical AI development by applying Guard Rails, Responsible AI principles, and staying compliant with AI Ethics and Regulations.
• Deploy AI models using Docker, Kubernetes and other Gen AI deployment tools.
• Collaborate with cross-functional teams to deliver scalable and secure AI solutions.
• Stay updated with the latest advancements in multi-modal AI, LLMs, and cloud-native AI platforms.
Experience with any of the following Cloud Native AI Services:
• Azure: Azure AI Foundry, Azure AI Agents, Azure AI Search, Azure Open AI, Document Intelligence,
• AWS: Bedrock, Amazon Q, Amazon Sage Maker
• Google Cloud Platform (GCP): Vertex AI, Model Garden, Agentspace, Cloud Run, GKE, Agent Engine , Compute Engine
• Experience with CI/CD pipelines (Azure Dev Ops, Git Lab CI, Jenkins), infrastructure as code (Terraform, Cloud Formation, Azure Bicep), and monitoring/logging tools.
Qualifications:
• Bachelor’s / master’s degree in engineering or technology
• Proven experience in building and deploying AI/ML solutions in production.
• Strong understanding of AI model lifecycle, MLOps, and cloud-native architectures.
• Excellent problem-solving, communication, and collaboration skills.
Certifications: Azure AI Engineer, AWS Certified Machine Learning, Google Cloud Professional ML Engineer, and relevant AI/ML certifications.
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