Senior AI Agents Programmer
At a Glance
- Category
- 💻 Technology
- Level
- Mid-Level
- Type
- Full-time
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Company Description
Kanz is partnering with BST to build an inclusive and just employment ecosystem. Therefore, we prioritize individuals coming from all walks of life.
Company Name: BST
Job Description
Design, build, and deploy AI agents that can plan, use tools, and complete tasks reliably while monitoring performance, safety, and cost.
Job Duties and Responsibilities
• Ship working agent workflows in production
• Improve task success rate through iteration
• Reduce latency and inference costs
• Implement guardrails and safety evaluations
• Monitor and optimize agent performance continuously
Qualifications
Required Qualifications
- Proficiency in Python and software engineering fundamentals
- Experience building LLM-based applications
- Understanding of agent architectures (planning, tools, memory)
- Familiarity with evaluation, testing, and monitoring for AI systems
- Experience with APIs, distributed systems, and deployment pipelines
- Python
- LLM frameworks (LangChain/LlamaIndex)
- Agent frameworks and tool use
- Prompting and function calling
- Vector databases (e.g., Pinecone/FAISS)
- Evaluation/observability (e.g., LangSmith)
- APIs and microservices
- CI/CD and cloud deployment
- Problem-solving and systems thinking
- Attention to detail and reliability
Requirements
- •Proficiency in Python and software engineering fundamentals
- •Experience building LLM-based applications
- •Understanding of agent architectures (planning, tools, memory)
- •Familiarity with evaluation, testing, and monitoring for AI systems
- •Experience with APIs, distributed systems, and deployment pipelines
- •Knowledge of Python LLM frameworks (LangChain/LlamaIndex)
- •Experience with Agent frameworks and tool use
- •Experience with Prompting and function calling
Nice to Have
- •Vector databases (e.g., Pinecone/FAISS)
- •Evaluation/observability (e.g., LangSmith)
- •APIs and microservices
- •CI/CD and cloud deployment
Responsibilities
- •Ship working agent workflows in production
- •Improve task success rate through iteration
- •Reduce latency and inference costs
- •Implement guardrails and safety evaluations
- •Monitor and optimize agent performance continuously
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