Gen. AI Engineer

About the role

We are seeking a hands-on Senior GenAI / Agentic AI Engineer to design, build, and deploy enterprise-scale AI applications that leverage Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), Agentic AI, and cloud-native technologies. The role focuses on building production-ready AI platforms from architecture through deployment.

Responsibilities

• Design and develop enterprise Generative AI applications using LLMs, RAG, Graph RAG, and multi-agent architectures.
• Build scalable document ingestion, embedding, retrieval, and vector search pipelines.
• Develop AI agents using frameworks such as LangChain, LangGraph, CrewAI, LlamaIndex, AutoGen, or similar technologies.
• Create secure backend services and APIs using Python, FastAPI, Flask, or comparable frameworks.
• Build intuitive AI-powered web applications using modern front-end technologies such as React, Angular, or Next.js.
• Deploy cloud-native AI solutions across AWS, Azure, and GCP using Docker, Kubernetes, and Infrastructure-as-Code.
• Implement observability, monitoring, LLMOps, and governance to ensure production reliability and responsible AI practices.
• Collaborate with product, engineering, architecture, and business stakeholders to deliver enterprise AI solutions.

Requirements

• 8+ years of software engineering, cloud engineering, AI/ML, or platform engineering experience.
• 3+ years of hands-on experience building production Generative AI or LLM-based applications.
• Strong expertise with Python and API development using FastAPI, Flask, or similar frameworks.
• Experience building Retrieval-Augmented Generation (RAG) solutions and working with vector databases such as Pinecone, Weaviate, Chroma, Milvus, or Azure AI Search.
• Experience with Agentic AI frameworks including LangChain, LangGraph, CrewAI, LlamaIndex, Semantic Kernel, or AutoGen.
• Strong understanding of prompt engineering, tool calling, agent orchestration, and workflow automation.
• Experience developing cloud-native applications on AWS, Azure, or GCP.
• Hands-on experience with Docker, Kubernetes, Terraform, CI/CD pipelines, and modern DevOps practices.
• Experience integrating enterprise AI applications with databases, APIs, and business systems.
• Strong understanding of security, governance, and responsible AI best practices.

Preferred Qualifications

• Experience implementing Graph RAG and knowledge graph solutions.
• Experience with MCP (Model Context Protocol) architecture.
• Experience deploying models using vLLM, Hugging Face, Triton, or TensorRT-LLM.
• Experience with Databricks, Spark, Kafka, Snowflake, or modern data engineering platforms.
• Experience building AI applications within regulated industries such as Financial Services, Healthcare, or Insurance.
• Azure, AWS, Google Cloud, or Databricks AI certifications.

Technical Environment

• Languages: Python, JavaScript/TypeScript, SQL
• Frameworks: LangChain, LangGraph, CrewAI, LlamaIndex, FastAPI, Flask, React, Angular, Next.js
• Cloud: AWS, Azure, GCP
• Vector Databases: Pinecone, Weaviate, Chroma, Milvus, Azure AI Search
• DevOps: Docker, Kubernetes, Terraform, GitHub Actions, Jenkins
• AI Platforms: OpenAI, Claude, Gemini, Llama, AWS Bedrock, Azure OpenAI