Description

You will architect enterprise-scale Generative AI solutions, defining the AI architecture strategy with a focus on scalability, security, and ethical principles.

Responsibilities

• Design and implement scalable AI microservices and APIs using LLMs, embeddings, and RAG pipelines integrated into Azure, AWS, or GCP.
• Lead PoCs for intelligent automation, code generation, and predictive analytics while introducing multi-agent orchestration frameworks like LangChain and LangGraph.
• Define AI Assurance strategies and key metrics to test and monitor AI systems, collaborating with platform teams to automate reporting.
• Partner with clients to translate business problems into technical blueprints, leading RFP responses and demonstrating Gen AI ROI.
• Oversee AI model lifecycle management (MLOps/LLMOps) and guide engineering teams in adopting AI-first design principles and governance standards.

Required Skills

• 15+ years in software engineering or solution architecture, including 5+ years in AI/ML, NLP, or Generative AI system design.
• Deep knowledge of LLMs, vector databases, and orchestration frameworks (LangChain, LangGraph, LlamaIndex).
• Strong understanding of RAG architecture, fine-tuning (LoRA/QLoRA), embeddings, and prompt engineering.
• Expertise in cloud-native AI platforms: Azure OpenAI, AWS Bedrock, Vertex AI, or similar.
• Proven ability to integrate AI models into enterprise systems using APIs, microservices, and event-driven architectures.
• Experience building reusable AI accelerators, tools, and frameworks for standardizing deployment and governance.
• Excellent communication skills to translate AI concepts into business value for clients and stakeholders.

Preferred Skills

• Experience leading enterprise AI transformations or building Gen AI Centers of Excellence.
• Background implementing agentic AI systems, AI copilots, or autonomous agents in real-world scenarios.

Key Skills

Education

Any Gradute