Cleared GenAI Engineer

Key Responsibilities:

• Design and construct generative AI use cases tailored to specific client requirements and data sets.
• Apply a deep understanding of Agentic AI frameworks, including their design principles and appropriate application scenarios.
• Develop and deploy generative AI models utilizing industry-leading platforms such as Amazon Kendra, Google Vertex AI, and AWS Bedrock.
• Implement advanced Large Language Models (LLMs) and transformer models to create sophisticated AI applications.
• Integrate AI solutions with cloud-based services, including Google Cloud Document AI, Google Cloud Gemini, Google Cloud Gemma, and Google Cloud Generative AI Language Models.
• Leverage NVIDIA AI Foundations and OpenAI ChatGPT for the development and refinement of AI applications.
• Utilize Databricks LLM and Databricks Mosaic AI for in-depth data analysis and efficient AI model development.
• Demonstrate a strong sense of curiosity, motivation, and a passion for experimentation and continuous learning to foster AI innovation applicable across diverse enterprise client domains.

Qualifications:

Must Have:

• Proven experience with modern generative AI capabilities, encompassing large language models, transformer models, and retrieval-augmented generation.
• Demonstrated experience building Retrieval-Augmented Generation (RAG) systems with vector databases like Pinecone, Weaviate, ChromaDB, or FAISS.
• Proficiency in modern software development languages such as Python, SQL, and Go for building generative AI solutions.
• An active Secret clearance is required.

Preferred:

• A strong understanding of AI ethics, responsible AI principles, and generative AI data ethics.
• Experience in LLM fine-tuning and LLMOps.
• The ability to author and validate prompts for AI models effectively.
• Familiarity with NVIDIA AI Foundations and OpenAI ChatGPT.
• Experience with AI-assisted development platforms, including Cursor, Windsurf, and Claude.
• Experience implementing tool-using AI agents to facilitate API integrations, enabling agents to access external data sources and web services.
• Experience with deployment and virtualization tools within containerized environments such as VMware, Docker, and Kubernetes.
• Proficiency in scripting with Linux Bash, PowerShell, or equivalent automation tools.