AI Software Engineer - Python

Job Description :

- Design and build AI-powered tools to improve software engineering and operational workflows across multiple product teams.

- Collaborate with technical leads and engineers to understand priorities, codebases, architecture, deployment, and engineering toil sources.

- Translate needs into practical AI solutions adopted by teams.

- Work hands-on in applied AI domains : developer productivity, workflow automation, retrieval and knowledge systems, agentic workflows, intelligent engineering tools.

- Ensure systems are evaluated, observable, reliable, and secure for daily engineering use.

- Communicate effectively to gather requirements, explain trade-offs, negotiate scope, and document decisions.

Requirements :

Must-have :

- 5-7 years of experience overall, 3+ years relevant.

- Strong software engineering experience in production apps, developer tools, automation, internal platforms, or data-intensive services.

- Proficiency in Python, TypeScript, Java, or similar.

- Experience delivering LLM-powered applications.

- Experience with retrieval-augmented generation, agentic AI workflows, AI developer/operational tools, workflow automation, AI evaluation/monitoring.

- Integration experience with Git, CI/CD, issue trackers, documentation, logging, feature flags, messaging.

- Strong understanding of APIs, testing, debugging, observability, authentication, error handling, secure delivery.

- Ability to learn unfamiliar systems by reading code and collaborating.

- Experience managing ambiguous problems through design, implementation, validation, rollout, adoption.

- Strong communication skills for technical collaboration.

- Ability to work independently and manage multiple priorities.

Nice-to-have :

- Experience with agent frameworks (LangGraph, OpenAI Agents SDK, LangChain).

- Knowledge systems (semantic/hybrid retrieval, embeddings, sources like Git, Confluence, Jira).

- Code intelligence/search (source-code indexing, dependency graphs, OpenSearch, Elasticsearch).

- Agentic system design (planning, tool use, memory, retries, human approval).

- Developer tooling and coding agents (Claude Code, Cursor, GitHub Copilot).

- Tool integration and automation (function calling, APIs, event-driven workflows).

- Evaluation and observability (synthetic evaluations, regression testing, LLM-as-judge, LangSmith, OpenTelemetry).

- Familiarity with AI models and security (Anthropic, OpenAI, Amazon Bedrock, guardrails, secure data handling).

- Systems and infrastructure (deployment using containers, APIs, workflow engines).

- Proven track record shipping AI tools impacting productivity, quality, reliability, cost, or operations.

(ref:hirist.tech)