What are the responsibilities and job description for the Sr. AI/ML Software Developer position at Jobs via Dice?
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Senior AI/ML Software Developer
Location : Austin Texas ( HYBRID - ONSITE Role )
Duration: 12 Months C2C
NOTE : Only Senior Level Experienced Candidates Workable
Overall IT Experience : Minimum 15 Years
ONLY LOCAL CANDIDATES
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.
Ii. Candidate Skills And Qualifications
Minimum Requirements:
experience in AI/ML engineering or advanced data science
Proven track record of building and deploying production-grade autonomous agents.
Strong experience in context engineering
Deep experience with LangChain, LangGraph, CrewAI, or AutoGPT.
Experience implementing RAG architectures using vector databases
Proficiency in Python and AI/ML libraries (OpenAI, Hugging Face, Azure AI)
Experience integrating LLMs via APIs Knowledge of AI governance, model lifecycle management, and evaluation
Experience implementing and extending the Model Context Protocol (MCP) to provide LLMs with secure, standardized access to local and remote data sources Experience implementing AI guardrails, content filtering, and safety controls
Understanding of data privacy and handling of sensitive data (PII/PHI)
Experience building multi-agent or autonomous agentic workflows
Experience optimizing LLM cost, token usage, and performance
Familiarity with enterprise AI deployment patterns and scalability considerations
Years
Experience
experience in AI/ML engineering or advanced data science
Proven track record of building and deploying production-grade autonomous agents.
Strong experience in context engineering
Deep experience with LangChain, LangGraph, CrewAI, or AutoGPT.
Experience implementing RAG architectures using vector databases
Proficiency in Python and AI/ML libraries (OpenAI, Hugging Face, Azure AI)
Experience integrating LLMs via APIs Knowledge of AI governance, model lifecycle management, and evaluation
Experience implementing and extending the Model Context Protocol (MCP) to provide LLMs with secure, standardized access to local and remote data sources Experience implementing AI guardrails, content filtering, and safety controls
Understanding of data privacy and handling of sensitive data (PII/PHI)
Experience building multi-agent or autonomous agentic workflows
Experience optimizing LLM cost, token usage, and performance
Familiarity with enterprise AI deployment patterns and scalability considerations
Senior AI/ML Software Developer
Location : Austin Texas ( HYBRID - ONSITE Role )
Duration: 12 Months C2C
NOTE : Only Senior Level Experienced Candidates Workable
Overall IT Experience : Minimum 15 Years
ONLY LOCAL CANDIDATES
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.
Ii. Candidate Skills And Qualifications
Minimum Requirements:
experience in AI/ML engineering or advanced data science
Proven track record of building and deploying production-grade autonomous agents.
Strong experience in context engineering
Deep experience with LangChain, LangGraph, CrewAI, or AutoGPT.
Experience implementing RAG architectures using vector databases
Proficiency in Python and AI/ML libraries (OpenAI, Hugging Face, Azure AI)
Experience integrating LLMs via APIs Knowledge of AI governance, model lifecycle management, and evaluation
Experience implementing and extending the Model Context Protocol (MCP) to provide LLMs with secure, standardized access to local and remote data sources Experience implementing AI guardrails, content filtering, and safety controls
Understanding of data privacy and handling of sensitive data (PII/PHI)
Experience building multi-agent or autonomous agentic workflows
Experience optimizing LLM cost, token usage, and performance
Familiarity with enterprise AI deployment patterns and scalability considerations
Years
Experience
experience in AI/ML engineering or advanced data science
Proven track record of building and deploying production-grade autonomous agents.
Strong experience in context engineering
Deep experience with LangChain, LangGraph, CrewAI, or AutoGPT.
Experience implementing RAG architectures using vector databases
Proficiency in Python and AI/ML libraries (OpenAI, Hugging Face, Azure AI)
Experience integrating LLMs via APIs Knowledge of AI governance, model lifecycle management, and evaluation
Experience implementing and extending the Model Context Protocol (MCP) to provide LLMs with secure, standardized access to local and remote data sources Experience implementing AI guardrails, content filtering, and safety controls
Understanding of data privacy and handling of sensitive data (PII/PHI)
Experience building multi-agent or autonomous agentic workflows
Experience optimizing LLM cost, token usage, and performance
Familiarity with enterprise AI deployment patterns and scalability considerations