We are seeking a 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. This is a hands-on engineering role focused on building production-ready AI platforms from architecture through deployment.
The ideal candidate has extensive experience developing scalable AI solutions, integrating LLMs into enterprise applications, and delivering secure, reliable systems that operate in production environments.
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.
- 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.
- 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.
- 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
Skills Required
- 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 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
- Experience implementing Graph RAG and knowledge graph solutions
- Experience with MCP 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
What We Do
When clients come to us for product design and development, they get a full range of technical expertise and laboratory resources, but they also get a team that’s relentless when it comes to solving problems and creating designs that are the ideal combination of function and form. What does that mean? We build tools that save lives. For surgeons, it means they have tools that not only address but anticipate their needs, which helps improve their patients’ outcomes. We create products that save money and ensure safety. For people juggling family, work, and other demands, it means they have products that provide real help in managing their everyday lives. And for businesses, it means their bottom line is supported with durable tools that improve efficiency and protect their teams. From concept to production plan, or at any phase in between, we are a partner that can extend your product development know-how or provide additional mindpower to free up in-house teams.
