Job Description

Job Title: Generative AI Engineer
Location: Charlotte, NC
Domain: Finance
Duration: Long Term Contract
Looking for W2 Candidates. No C2C

Job Description / Responsibilities:
Provide technical leadership in developing and implementing Generative AI solutions for enterprise-grade applications.
Design, fine-tune, and deploy large language models (LLMs) and multimodal models tailored to solve specific business challenges.
Collaborate with product managers and engineering teams to translate business problems into scalable AI solutions.
Architect, build, and operationalize MLOps pipelines to support the full lifecycle of generative AI models, including data preparation, model training, validation, deployment, and monitoring.
Lead POC development and evaluation of cutting-edge open-source and commercial AI models (e.g., GPT, Claude, Gemini, Mistral).
Advocate for and implement responsible AI practices including fairness, transparency, privacy, and security in model development and deployment.
Research and adopt state-of-the-art GenAI techniques such as Retrieval-Augmented Generation (RAG), prompt engineering, agent-based workflows (LangGraph, CrewAI), and vector database optimization.
Actively contribute to AI community discussions within the organization; share reusable frameworks, templates, and tools.
Develop reusable AI services using APIs and microservices architecture for integration into downstream platforms.

Required Qualifications:
7+ years of overall experience in data science/ML, with at least 3+ years focused on Generative AI, NLP, or foundation models.
Strong proficiency in Python and libraries like PyTorch, Hugging Face Transformers, LangChain, OpenAI SDK.
Expertise in LLM tuning techniques: instruction tuning, RLHF, quantization, LoRA, etc.
Experience with vector databases like FAISS, Pinecone, Weaviate, or Chroma.
Deep understanding of foundational models, tokenization strategies, and transformer architectures.
Hands-on experience building agentic frameworks (LangGraph, AutoGen, CrewAI).
Experience working with GPU environments and distributed training setups.
Familiarity with Azure AI Studio, Vertex AI, or AWS Bedrock for cloud-native model deployment.
Strong understanding of secure APIs, scalable inference infrastructure, and observability frameworks (e.g., Prometheus, Grafana).

Best Regards:
Tarun K
Phone: +1-469-848-1728

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