What are the responsibilities and job description for the Generative AI Engineer (USC) Preffered position at SATCON Inc?
Title: Generative AI Engineer
Locations: Dallas, TX | Charlotte, NC (Hybrid/Onsite)
Duration: Long-term Contract
Job Overview
We are seeking a forward-thinking Generative AI Engineer to join our Engineering team. In this role, you will bridge the gap between cutting-edge research and scalable production applications. You will be responsible for designing, fine-tuning, and deploying Large Language Models (LLMs) and diffusion models to solve complex business challenges.
The ideal candidate doesn''''''''t just use AI APIs but understands the underlying architecture of transformers, the nuances of prompt engineering, and the infrastructure required to scale GenAI solutions in an enterprise environment.
Key Responsibilities
Model Implementation: Design and deploy production-ready applications using LLMs (GPT-4, Claude, Llama 3) and specialized frameworks.
RAG Architecture: Build and optimize Retrieval-Augmented Generation (RAG) pipelines using vector databases to provide models with domain-specific context.
Fine-Tuning: Perform supervised fine-tuning (SFT) and utilize techniques like PEFT (LoRA/QLoRA) to adapt open-source models to specific tasks.
Prompt Engineering: Develop and iterate on complex prompt templates and agentic workflows (Chain-of-Thought, ReAct).
Evaluation & Observability: Implement rigorous evaluation frameworks (e.g., RAGAS, G-Eval) to measure model hallucinations, bias, and performance.
Deployment: Containerize AI services using Docker/Kubernetes and manage model serving via tools like vLLM, TGI, or BentoML.
Technical Qualifications
Languages: Proficiency in Python is mandatory (PyTorch or TensorFlow experience preferred).
GenAI Frameworks: Extensive experience with LangChain, LlamaIndex, or Haystack.
Vector Databases: Hands-on experience with Pinecone, Milvus, Weaviate, or ChromaDB.
LLM Ops: Familiarity with the end-to-end lifecycle of AI models, including versioning (MLflow, WandB) and monitoring.
Cloud Infrastructure: Experience deploying AI solutions on AWS (SageMaker/Bedrock) or Azure (OpenAI Service).
API Design: Strong skills in building robust APIs (FastAPI/Flask) to integrate AI features into frontend applications.
Education & Experience
Bachelor’s or Master’s degree in Computer Science, Data Science, or a related quantitative field.
2 years of direct experience working with Generative AI technologies.
5 years of overall software engineering or machine learning experience.
Soft Skills
Problem Solver: Ability to navigate the hallucination challenges of GenAI with creative engineering solutions.
Communicator: Capable of explaining complex AI concepts to non-technical stakeholders.
Adaptable: The GenAI landscape changes weekly; a passion for continuous learning is essential.
Note to Applicants: Please ensure your resume highlights specific GenAI projects, including the models used and the business impact of the deployment.