Generative AI Engineer

BlackCube Labs; Charlotte (NC); Hybrid; USD 140,000 - 210,000; Full time

Job summary

BlackCube Labs in Charlotte, NC (Hybrid) seeks a Generative AI Engineer with a strong Python background to design, develop, and deploy cutting-edge AI solutions. The role focuses on LLMs, prompt engineering, and agentic AI frameworks, building scalable applications and advanced RAG systems.

Ideal candidates have extensive experience with LangChain, LangGraph, vector databases, and MLOps, plus cloud proficiency (AWS/GCP/Azure) for enterprise AI delivery and microservice APIs using FastAPI and

Qualifications

• 10 years of hands-on experience in AI, data science and ML.
• 2+ years in Agentic AI, VLLMs and LangChain.
• Experience designing and deploying Retrieval-Augmented Generation pipelines.

Responsibilities

• Design and deploy Generative AI models and RAG pipelines.
• Develop prompt engineering strategies and embedding-based retrieval.
• Integrate Gen AI capabilities into web apps and enterprise workflows.

Skills

Gen AI

LLM

RAG

LangChain

Lang Graph

Python

ML Ops

REST API

FastAPI

VLLM

Agentic AI

Embedding retrieval

Vector DBs

Cloud platforms

Pinecone

FAISS

ChromaDB

Tools

Docker

Kubernetes

FastAPI

Pydantic

JSON schemas

CI/CD

Job description

Job Title: Generative AI Engineer

Location: Charlotte, NC 28202 (Hybrid)

Duration: 12 Months

Interview Process (Is face to face required): Yes (2nd round)

Must Have Skills

• GEN AI
• Agentic AI
• VLLM
• fAST API
• REST API
• ClientD
• Lang Graph
• Lang Chain
• Graph RAG
• ML Ops
• Python
• ML
• Data Science
• RAG
• LLM

Nice to Have Skills

• GCP
• Prompt Engineering

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 ClientP 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 ClientP 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

Minimum Years of Experience

• 10 years

Top 3 responsibilities

• Strong experience in GEN AI, LLM, RAG,ML, DL,ML Ops, LLMOps, Cloud platform, Model servicing optimization, Python
• Strong communication skills
• Strong programming skills