Sr. Gen AI Engineer

Job Title: Sr. Gen AI Engineer

Work Location & Reporting Address: Charlotte, NC 28202 (Onsite-Hybrid. Client!!!)

Contract duration: 12+ Months

Interview Process (Is face to face required?): Face to face interview

Must Have Skills:

GEN AI

Agentic AI

VLLM

fAST API

REST API

MCD

Lang Graph

Lang Chain

Graph RAG

ML Ops

Python

ML

Data Science

RAG

LLM

Nice to Have Skills:

Eligible to workP

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 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 Eligible to workP 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 LanEligible to workhain, 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 Eligible to workP (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, Eligible to workP 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 Eligible to workP 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

Certifications Needed:

Top 3 responsibilities you would expect the Subcon to shoulder and execute:

Strong experience in GEN AI, LLM, RAG,ML, DL,ML Ops, LLMOps, Cloud platform,Model servicing Eligible to workimization, Python

Strong communication skills

Strong programming skills

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