AI Software Engineer (LLM & Automation)

Accelon Consulting

Required skills

Website: accelonconsulting.com
Job details:

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.

Required Skills & Qualifications

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.

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