AI / ML Engineer ( F2F Interview at Austin, Texas)

Job Title: AI/ ML Engineer

Duration: 5 Months

Location: Austin, TX

Consultants local to texas who can do In Person interview at Austin, Texas

Job Summary:

• Researching, designing, implementing and managing software programs.
• Testing and evaluating new programs.
• Working closely with other developers, UX designers, business and systems analysts.
• 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.

Skills Required:

• 4 years of experience required in AI/ML engineering or advanced data science
• 4 years of experience required with a proven track record of building and deploying production-grade autonomous agents
• 4 years of experience required in context engineering
• 4 years of experience required with deep experience in LangChain, LangGraph, CrewAI, or AutoGPT
• 4 years of experience required in implementing RAG architectures using vector databases
• 4 years of experience required with proficiency in Python and AI/ML libraries (OpenAI, Hugging Face, Azure AI)
• 4 years of experience required in integrating LLMs via APIs and knowledge of AI governance, model lifecycle management, and evaluation
• 4 years of experience required in implementing and extending the Model Context Protocol (MCP) to provide LLMs with secure, standardized access to local and remote data sources and implementing AI guardrails, content filtering, and safety controls
• 4 years of experience required with understanding of data privacy and handling of sensitive data (PII/PHI)

Skills Preferred:

• 2 years of experience preferred in building multi-agent or autonomous agentic workflows
• 2 years of experience preferred in optimizing LLM cost, token usage, and performance
• 2 years of experience preferred with familiarity in enterprise AI deployment patterns and scalability considerations