What are the responsibilities and job description for the AI/ML Software Developer position at Jobs via Dice?
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Position – AI/ML Engineer
Location - Austin, TX 78751
Position note- ONSITE/Hybrid at the location listed above (with some hybrid work – determined by the hiring manager). The program will only accept LOCAL ONLY candidates for this position.
Level Description
Position – AI/ML Engineer
Location - Austin, TX 78751
Position note- ONSITE/Hybrid at the location listed above (with some hybrid work – determined by the hiring manager). The program will only accept LOCAL ONLY candidates for this position.
Level Description
- 4-7 years of experience in the field or in a related area.
- Familiar with standard concepts, practices, and procedures within a particular field.
- It relies on limited experience and judgment to plan and accomplish goals.
- A certain degree of creativity and latitude is required.
- Works under limited supervision with considerable latitude for the use of initiative and independent judgment.
- Researching, designing, implementing and managing software programs. Testing and evaluating new programs.
- Working closely with other developers, UX designers, business and systems analysts.
- Additional job details and special considerations
- AI Agent Engineer Designs and develops AI-driven agentic solutions, including autonomous workflows and Retrieval-Augmented Generation (RAG) systems, to enhance productivity, automate processes, and support intelligent decision-making with a focus on governance, security, and cost efficiency.
- Experience in AI/ML engineering or advanced data science. Required 4 year
- Proven track record of building and deploying production-grade autonomous agents. Required 4 year
- Strong experience in context engineering. Required 4 year
- Experience implementing RAG architectures using vector databases. Required 4 year
- Proficiency in Python and AI/ML libraries (OpenAI, Hugging Face, Azure AI) Required 4 year
- Experience integrating LLMs via APIs Knowledge of AI governance, model lifecycle management, and evaluation. Required 4 year
- Experience implementing and extending the Model Context Protocol (MCP) to provide LLMs with secure, standardized access to local and remote data sources. Required 4 year
- Experience implementing AI guardrails, content filtering, and safety control. Required 4 year
- Understanding of data privacy and handling of sensitive data (PII/PHI). Required 4 year
- Experience building multi-agent or autonomous agentic workflows. Preferred 2 year
- Experience optimizing LLM cost, token usage, and performance. Preferred 2 year
- Familiarity with enterprise AI deployment patterns and scalability considerations. Preferred 2 year