Job Description

Job Title: Generative AI Engineer

Location: Austin, TX

Domain: Automotive

Duration: Long Term Contract

Work Authorization: W2 candidates only (No C2C)

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 :

• 8+ years of overall experience in data science/ML, with at least 8 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, Autogeny, 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:

Tina
Phone: +1-469-899-9341
Email: tina@technipros.com

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