GEN AI Engineer

IMCS Group; Charlotte (NC); Hybrid; USD 180,000 - 240,000; Full time

Job summary

IMCS Group is seeking a Lead Generative AI Engineer in Charlotte, NC to design, develop, and deploy cutting-edge AI solutions. This onsite-hybrid role focuses on Gen AI frameworks, LLMs, and agentic AI, with emphasis on scalable, enterprise-grade deployments.

You will lead building retrieval-augmented pipelines, memory-driven multi-agent systems, and MLOps-enabled workflows across AWS, Azure, and GCP. Strong Python and FastAPI-based microservices are required.

Qualifications

• 7+ years hands-on experience in AI, Data science, ML, GEN AI.
• 2 years hands-on experience in Agentic AI, VLLM’s, GEN AI, Lang Chain, Lang Graph, RAG, LLM OPS and AI Services in GCP and Azure.
• Extensive experience with LangChain, LangGraph, and agentic AI patterns including routing, memory, multi-agent orchestration, guardrails, and failure recovery.

Responsibilities

• Design and implement Generative AI models for text, image, or multimodal applications.
• Develop prompt engineering strategies and embedding-based retrieval systems.
• Integrate Gen AI capabilities into web applications and enterprise workflows.
• Build agentic AI applications with context engineering and MCP tools.

Skills

Generative AI

LLMs & Prompting

Agentic AI

MLOps

Python

LangChain/LangGraph

RAG

Cloud platforms

Microservices/API

Vector databases

Tools

FastAPI

Docker

Kubernetes

PyTorch/TensorFlow

GitHub Actions

Job description

In person Interview Required at Charlotte, NC -LOCAL CANDIDATES ONLY!!!

Job Title: Lead Generative AI Engineer

Work Location: Charlotte, NC (Onsite-Hybrid. LOCAL CANDIDATES ONLY!!!)

Detailed Job Description:

We are seeking a highly skilled Generative AI Engineer with a strong Python background to design, develop, and deploy cutting‑edge AI solutions. The ideal candidate will have hands‑on experience with Large Language Models (LLMs), prompt engineering, and Gen AI frameworks, along with expertise in building scalable AI applications. Experience in Developing Agentic AI solutions.

Key Responsibilities:

• Design and implement Generative AI models for text, image, or multimodal applications.
• Develop prompt engineering strategies and embedding-based retrieval systems.
• Integrate Gen AI capabilities into web applications and enterprise workflows.
• Build agentic AI applications with context engineering and MCP tools.

Required Skills & Qualifications:

• 7+ years of hands‑on experience in AI, Data science, ML, GEN AI
• 2 years of strong hands‑on experience in Agentic AI, VLLM’s, GEN AI, Lang Chain, Lang Graph, RAG, LLM OPS and AI Services in GCP and Azure.
• Strong hands‑on experience designing and deploying Retrieval-Augmented Generation (RAG) pipelines
• Strong MLOps/LLMOps experience with CI/CD automation,
• Extensive experience with LangChain, LangGraph, and agentic AI patterns including routing, memory, multi‑agent orchestration, guardrails, and failure recovery.
• Experience in Cloud‑native engineering across AWS (SageMaker, Lambda, ECS/Fargate, S3, API Gateway, Step Functions) and GCP (Vertex AI) for scalable AI delivery
• Experience in Developing microservices and API development using FastAPI, REST APIs, Pydantic/JSON schemas, Docker, and Kubernetes for low‑latency serving.
• Strong Hands‑on experience with vector databases and semantic search technologies including Pinecone, FAISS, ChromaDB, and embedding lifecycle management
• Strong proficiency in Python and AI/ML frameworks (PyTorch, TensorFlow).
• Hands on experience using session and memory for building multi‑agent systems along with using MCP tools.
• Hands‑on experience with LLMs, transformers, and Hugging Face ecosystem.
• Knowledge and experience with vector databases and RAG technique for semantic search.
• Familiarity with cloud AI services (AWS SageMaker, Azure OpenAI, GCP Vertex AI).
• Understanding of MLOps practices for scalable AI deployment.
• Strong experience in working with LLM fine‑tuning with LoRA, QLoRA, PEFT,
• Strong experience in Architected advanced RAG systems using Pinecone, FAISS, Weaviate, Chroma, hybrid retrieval, and custom embeddings,
• Strong experience in Designing end‑to‑end LLMOps/MLOps pipelines using MLflow, DVC, SageMaker Pipelines, Vertex AI Pipelines, and GitHub Actions
• Experience in using cloud‑native AI systems on AWS (SageMaker, Lambda, EKS, EC2, Step Functions, S3, Glue) and GCP Vertex AI, supporting high‑volume inference and secure enterprise operations
• Experience in developing multi‑agent orchestration workflows using LangGraph and CrewAI for tool‑calling, validation agents, automated reasoning, and workflow supervision