Software Engineer Jobs

About The Position

Requirements

• 5–8 years of relevant experience.
• Bachelor's or master's degree in Computer Science, AI, ML, or a related field, or equivalent experience.
• Hands-on experience with LLMs such as OpenAI, Anthropic, Gemini, Llama, or equivalent.
• Familiarity with agentic frameworks such as LangGraph, LangChain, CrewAI, or similar, and agentic SDKs appropriate to the level.
• Understanding of prompt engineering, tool/function calling, planning, reasoning, and workflow orchestration.
• Python experience.
• Experience with REST APIs, microservices, cloud, containers, CI/CD, and testing, with depth appropriate to the level.
• Experience with evaluations for LLM, RAG, or agent use cases.
• Experience with observability tools such as Langfuse, OpenSearch, or OpenTelemetry, as appropriate to the level.
• Strong communication and ownership mindset.
• Ability to set technical direction for an AI area, rather than only deliver features.
• Ability to balance innovation speed with enterprise quality, safety, and cost.
• Ability to support multi-team adoption of patterns or practices led.

Nice To Haves

• Experience with frontier or foundation models beyond API usage, including evaluation, adaptation, or fine-tuning.
• Experience with open-weight models and local inference, such as vLLM, Ollama, or Hugging Face.
• Data exploration techniques and tools.
• Agile or Atlassian experience.
• Experience with advanced IDEs such as Cursor, IntelliJ, or VSCode.

Responsibilities

• Design, develop, and maintain enterprise-grade agentic AI applications using modern LLMs and AI frameworks.
• Build autonomous and semi-autonomous AI agents capable of planning, reasoning, decision-making, and task execution.
• Develop multi-agent systems and orchestrate interactions among agents, tools, APIs, and enterprise applications.
• Integrate AI agents with internal and external services through APIs, databases, messaging systems, and workflow engines.
• Build and deploy RAG solutions using vector databases and enterprise knowledge sources.
• Optimize AI workflows for performance, scalability, reliability, and cost efficiency.
• Implement observability, monitoring, guardrails, and governance mechanisms for production AI systems.
• Build and run agent evaluations, define evaluation sets, test behavior, and iterate on prompts, tools, and workflows.
• Package and deploy solutions in production and apply diagnostics across the AI stack.
• Collaborate with product teams, architects, and stakeholders; contribute to reviews, documentation, and best practices.
• Define shared agentic patterns, evaluation practices, and production standards across teams.
• Lead architecture for cross-cutting AI capabilities used by multiple products or squads.
• Coach AI engineers and raise standards for quality, safety, and cost discipline.
• Partner with AI Quality, SRE, and Security on release gates and operational practices.
• Set technical direction for an AI area and support multi-team adoption of patterns or practices.

Benefits

• Country-specific benefits may be available.
• A variable compensation plan may be available.