C
Bizoforce Hiring - AI Engineer Agentic Systems | Austin, Texas, USA
Confidential
5 hours ago
Full-time
On-site
Experience (in Years)
Required Experience: 5–10 years. Relevant Experience: At least 5 years in software engineering, machine learning, or data engineering, including a minimum of 1 year building LLM-based or Generative AI applications. Technical Expertise: Strong hands-on experience with Python, LLMs, RAG, AI systems, and agent orchestration frameworks. Agentic AI Experience: Experience developing LLM-powered agents, tool-calling workflows, and multi-agent systems. Cloud and Database Expertise: Experience with Azure databases or cloud data platforms, SQL, Docker, and CI/CD pipelines. Domain Experience: Experience building AI tools and workflows for server validation is highly relevant. Required Skills
Programming: Strong Python scripting and software engineering skills, including production code, testing, packaging, asynchronous programming, and API development. Agentic AI Systems: Experience designing and developing LLM-powered agents that plan, use tools, and execute multi-step workflows. LLM Orchestration: Hands-on experience with LangChain, LangGraph, LlamaIndex, Semantic Kernel, or equivalent frameworks. Retrieval-Augmented Generation (RAG): Experience building RAG pipelines involving chunking, embeddings, vector search, hybrid search, re-ranking, and context assembly. Large Language Models: Practical knowledge of prompt engineering, structured outputs, function calling, and context-window management. Token Optimization: Ability to optimize token utilization, reduce unnecessary context, and balance cost, latency, and response quality. Database and SQL: Strong SQL skills and experience querying enterprise databases and structured data sources. Azure: Experience working with Azure databases and cloud data platforms. Tool Integration: Ability to integrate AI agents with APIs, databases, and enterprise systems. Backend Development: Experience developing production Python services and APIs using FastAPI or similar frameworks. Containerization and Deployment: Experience with Docker, CI/CD pipelines, and deploying services on cloud platforms. Machine Learning: Understanding of machine learning fundamentals and familiarity with PyTorch, TensorFlow, or Hugging Face Transformers. Testing and Evaluation: Experience validating AI workflows, testing application behavior, and improving reliability. Communication: Strong written and verbal communication skills, with the ability to clarify ambiguous requirements and communicate technical decisions. Preferred Skills / Good-to-Have Technical Experience
Experience developing Model Context Protocol (MCP) servers or similar agent tool-integration layers. Production experience with multi-agent architectures involving planner, executor, reviewer, and specialist agents. Familiarity with human-in-the-loop checkpoints, approval gates, retries, and failure recovery. Experience with LLM observability and evaluation tools such as LangSmith, Langfuse, Weights & Biases, or MLflow. Experience with workflow orchestration tools such as Airflow, Dagster, or Prefect. Familiarity with DBT and analytics engineering practices. Experience with model fine-tuning or parameter-efficient adaptation techniques such as LoRA/PEFT. Knowledge of AI governance, responsible AI, and enterprise security requirements. Experience with semiconductor, hardware validation, manufacturing, server validation, or other technical engineering domains. Experience building user-facing AI interfaces using Streamlit, chat interfaces, dashboards, or embedded integrations. Familiarity with Snowflake, Databricks, FAISS, pgvector, Pinecone, or Chroma. Qualifications
Bachelor's or Master's degree in Computer Science, Engineering, Data Science, Artificial Intelligence, or a related field. At least 5 years of professional experience in software engineering, machine learning, or data engineering. Minimum 1 year of hands-on experience developing LLM-based or Generative AI applications. Demonstrated ability to build and deploy production-grade Python applications and AI services. Practical experience developing RAG applications and integrating LLM orchestration frameworks. Strong understanding of SQL, Azure databases, API development, and cloud deployment practices. Experience with automated testing, version control, and CI/CD processes. Strong analytical and problem-solving skills, with the ability to deliver reliable AI solutions for complex technical workflows. Agent Design and Orchestration
Design, develop, and deploy LLM-powered agents capable of planning, calling tools, and completing multi-step tasks. Build agent workflows using LangChain, LangGraph, or equivalent orchestration frameworks. Implement multi-agent architectures involving planner, executor, reviewer, and specialist roles. Develop state management, hand-off logic, retry mechanisms, and graceful failure-handling processes. Design and expose agent tools through function calling, MCP servers, and secure integrations with internal APIs, databases, and enterprise systems. Retrieval and Knowledge Systems
Build and operationalize RAG pipelines using document chunking, embeddings, vector search, hybrid search, re-ranking, and context assembly. Integrate structured, semi-structured, and unstructured data from enterprise sources such as SharePoint, Confluence, Jira, REST APIs, Excel, and SQL warehouses. Improve response accuracy and grounding through retrieval optimization, prompt engineering, and effective context management. Reduce hallucinations by improving the relevance and quality of retrieved information. Optimize token consumption while maintaining response quality and controlling inference costs. Python Engineering and Backend Development
Develop production-ready Python services and APIs for AI agents and model inference. Build backend services using FastAPI or equivalent frameworks. Apply asynchronous programming, testing, packaging, and clean coding practices. Engineering, Deployment, and Monitoring
Containerize AI applications using Docker and deploy them through established CI/CD pipelines. Support deployments on Azure, AWS, or GCP, as applicable. Implement structured logging, tracing, latency monitoring, token tracking, and cost accounting. Apply MLOps and LLMOps practices, including prompt and configuration versioning, automated regression testing, and controlled releases. Troubleshoot production issues and improve application reliability, scalability, and performance. Evaluation and Quality Assurance
Develop evaluation frameworks for agent workflows using golden datasets, task-level success metrics, and regression test suites. Use LLM-as-judge techniques and other evaluation methods to assess agent performance. Measure and optimize accuracy, latency, and operational cost. Validate tool-calling behavior, retrieval quality, and multi-step workflow execution. Ensure changes to models, prompts, and agent configurations are tested before release. Collaboration and Continuous Improvement
Collaborate with domain experts, data engineers, product stakeholders, and engineering teams to automate manual workflows. Convert ambiguous technical problems into clearly defined agent workflows and production-ready solutions. Deliver AI capabilities through practical interfaces such as Streamlit applications, chat interfaces, dashboards, and embedded integrations. Prepare technical design documents, operational runbooks, and implementation notes. Participate in code reviews, design reviews, and technical discussions. Follow enterprise security, responsible AI, and governance requirements when developing AI solutions. Assignment Details
Job Location: Austin, Texas, USA Work Mode: On-site Job Type: Full Time Open Positions: Multiple Working Hours: 9:00 AM – 5:00 PM Hours per Week: 40 hours Timesheet Cycle: Weekly Job Status: Active
#J-18808-Ljbffr
Required Experience: 5–10 years. Relevant Experience: At least 5 years in software engineering, machine learning, or data engineering, including a minimum of 1 year building LLM-based or Generative AI applications. Technical Expertise: Strong hands-on experience with Python, LLMs, RAG, AI systems, and agent orchestration frameworks. Agentic AI Experience: Experience developing LLM-powered agents, tool-calling workflows, and multi-agent systems. Cloud and Database Expertise: Experience with Azure databases or cloud data platforms, SQL, Docker, and CI/CD pipelines. Domain Experience: Experience building AI tools and workflows for server validation is highly relevant. Required Skills
Programming: Strong Python scripting and software engineering skills, including production code, testing, packaging, asynchronous programming, and API development. Agentic AI Systems: Experience designing and developing LLM-powered agents that plan, use tools, and execute multi-step workflows. LLM Orchestration: Hands-on experience with LangChain, LangGraph, LlamaIndex, Semantic Kernel, or equivalent frameworks. Retrieval-Augmented Generation (RAG): Experience building RAG pipelines involving chunking, embeddings, vector search, hybrid search, re-ranking, and context assembly. Large Language Models: Practical knowledge of prompt engineering, structured outputs, function calling, and context-window management. Token Optimization: Ability to optimize token utilization, reduce unnecessary context, and balance cost, latency, and response quality. Database and SQL: Strong SQL skills and experience querying enterprise databases and structured data sources. Azure: Experience working with Azure databases and cloud data platforms. Tool Integration: Ability to integrate AI agents with APIs, databases, and enterprise systems. Backend Development: Experience developing production Python services and APIs using FastAPI or similar frameworks. Containerization and Deployment: Experience with Docker, CI/CD pipelines, and deploying services on cloud platforms. Machine Learning: Understanding of machine learning fundamentals and familiarity with PyTorch, TensorFlow, or Hugging Face Transformers. Testing and Evaluation: Experience validating AI workflows, testing application behavior, and improving reliability. Communication: Strong written and verbal communication skills, with the ability to clarify ambiguous requirements and communicate technical decisions. Preferred Skills / Good-to-Have Technical Experience
Experience developing Model Context Protocol (MCP) servers or similar agent tool-integration layers. Production experience with multi-agent architectures involving planner, executor, reviewer, and specialist agents. Familiarity with human-in-the-loop checkpoints, approval gates, retries, and failure recovery. Experience with LLM observability and evaluation tools such as LangSmith, Langfuse, Weights & Biases, or MLflow. Experience with workflow orchestration tools such as Airflow, Dagster, or Prefect. Familiarity with DBT and analytics engineering practices. Experience with model fine-tuning or parameter-efficient adaptation techniques such as LoRA/PEFT. Knowledge of AI governance, responsible AI, and enterprise security requirements. Experience with semiconductor, hardware validation, manufacturing, server validation, or other technical engineering domains. Experience building user-facing AI interfaces using Streamlit, chat interfaces, dashboards, or embedded integrations. Familiarity with Snowflake, Databricks, FAISS, pgvector, Pinecone, or Chroma. Qualifications
Bachelor's or Master's degree in Computer Science, Engineering, Data Science, Artificial Intelligence, or a related field. At least 5 years of professional experience in software engineering, machine learning, or data engineering. Minimum 1 year of hands-on experience developing LLM-based or Generative AI applications. Demonstrated ability to build and deploy production-grade Python applications and AI services. Practical experience developing RAG applications and integrating LLM orchestration frameworks. Strong understanding of SQL, Azure databases, API development, and cloud deployment practices. Experience with automated testing, version control, and CI/CD processes. Strong analytical and problem-solving skills, with the ability to deliver reliable AI solutions for complex technical workflows. Agent Design and Orchestration
Design, develop, and deploy LLM-powered agents capable of planning, calling tools, and completing multi-step tasks. Build agent workflows using LangChain, LangGraph, or equivalent orchestration frameworks. Implement multi-agent architectures involving planner, executor, reviewer, and specialist roles. Develop state management, hand-off logic, retry mechanisms, and graceful failure-handling processes. Design and expose agent tools through function calling, MCP servers, and secure integrations with internal APIs, databases, and enterprise systems. Retrieval and Knowledge Systems
Build and operationalize RAG pipelines using document chunking, embeddings, vector search, hybrid search, re-ranking, and context assembly. Integrate structured, semi-structured, and unstructured data from enterprise sources such as SharePoint, Confluence, Jira, REST APIs, Excel, and SQL warehouses. Improve response accuracy and grounding through retrieval optimization, prompt engineering, and effective context management. Reduce hallucinations by improving the relevance and quality of retrieved information. Optimize token consumption while maintaining response quality and controlling inference costs. Python Engineering and Backend Development
Develop production-ready Python services and APIs for AI agents and model inference. Build backend services using FastAPI or equivalent frameworks. Apply asynchronous programming, testing, packaging, and clean coding practices. Engineering, Deployment, and Monitoring
Containerize AI applications using Docker and deploy them through established CI/CD pipelines. Support deployments on Azure, AWS, or GCP, as applicable. Implement structured logging, tracing, latency monitoring, token tracking, and cost accounting. Apply MLOps and LLMOps practices, including prompt and configuration versioning, automated regression testing, and controlled releases. Troubleshoot production issues and improve application reliability, scalability, and performance. Evaluation and Quality Assurance
Develop evaluation frameworks for agent workflows using golden datasets, task-level success metrics, and regression test suites. Use LLM-as-judge techniques and other evaluation methods to assess agent performance. Measure and optimize accuracy, latency, and operational cost. Validate tool-calling behavior, retrieval quality, and multi-step workflow execution. Ensure changes to models, prompts, and agent configurations are tested before release. Collaboration and Continuous Improvement
Collaborate with domain experts, data engineers, product stakeholders, and engineering teams to automate manual workflows. Convert ambiguous technical problems into clearly defined agent workflows and production-ready solutions. Deliver AI capabilities through practical interfaces such as Streamlit applications, chat interfaces, dashboards, and embedded integrations. Prepare technical design documents, operational runbooks, and implementation notes. Participate in code reviews, design reviews, and technical discussions. Follow enterprise security, responsible AI, and governance requirements when developing AI solutions. Assignment Details
Job Location: Austin, Texas, USA Work Mode: On-site Job Type: Full Time Open Positions: Multiple Working Hours: 9:00 AM – 5:00 PM Hours per Week: 40 hours Timesheet Cycle: Weekly Job Status: Active
#J-18808-Ljbffr