Role overview
Softjourn is a full-cycle consulting and software development company, with expert product teams experienced in Fintech, Media & Entertainment, with a special emphasis on Ticketing. Headquartered in Silicon Valley, California, with R&D offices in Ukraine, Poland, and Brazil, Softjourn is a global software development company with over 20 years of experience.
Softjourn has been honored as a veteran-friendly business by the Veteran Hub in Ukraine. We are committed to creating a supportive environment for veterans and implementing processes that address their needs. We highly value the unique skills and perspectives that military veterans bring to our company and are dedicated to assisting their transition to the workforce.
Softjourn Inc. is an Equal Opportunity Employer. We celebrate diversity in all forms and are committed to maintaining a discrimination-free workplace that treats applicants and employees with dignity and respect. Our employment process is conducted without regard to race, color, religion, nationality or ethnic background, sex, pregnancy, sexual orientation, gender identity or expression, age, disability, protected veteran status, genetic information, or other attributes protected by state, federal, and local law.
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What you'll work on
- Design, build, and deploy LLM-based applications and agentic systems to production, from prototype to live system;
- Implement RAG pipelines, tool-using agents, and multi-step workflows over proprietary and third-party data sources;
- Evaluate foundation models (OpenAI, Anthropic, Gemini, Llama, and others) against client use cases, constraints, and budget;
- Build and maintain LLM evaluation pipelines, including offline benchmarks, online monitoring, and human-in-the-loop review processes;
- Measure and improve model performance iteratively across quality, relevance, faithfulness, latency, and cost;
- Deploy and serve AI systems on cloud platforms with attention to scalability, reliability, and cost efficiency;
- Work with Solution Architects and delivery teammates to translate ambiguous business problems into concrete technical tasks and solution components;
- Communicate technical decisions, limitations, and trade-offs clearly to teammates, product stakeholders, and clients when needed;
- Contribute reusable internal tools, skills, integrations, and MCP-style patterns that improve future client delivery;
- Stay current with the fast-moving AI landscape and evaluate emerging frameworks, tools, and approaches with a practical mindset;
- Making customization for the client.
What we're looking for
- 5+ years of primary software engineering experience building and shipping production systems;
- 2+ years of hands-on experience building and deploying LLM-based applications or AI agents in production environments;
- Strong software engineering fundamentals and the ability to write clean, maintainable, production-ready code;
- Experience building APIs, services, integrations, and data flows for production systems
- Experience designing and implementing agentic systems using major model providers and open-weight models (e.g. OpenAI, Anthropic, Gemini, Llama, or comparable);
- Hands-on experience with at least one orchestration framework (LangChain, LangGraph, AutoGen, CrewAI, etc.);
- Experience with LLM evaluation and benchmarking: designing evaluation pipelines, measuring model performance, and iterating systematically;
- Experience architecting RAG systems, including chunking strategies, embedding models, vector databases, and reranking (e.g. Pinecone, Weaviate, Qdrant, Chroma, Milvus);
- Strong prompt design and iteration skills;
- Experience building custom tools, skills, integrations, or protocol-based extensions for AI systems, including MCP-style server/client patterns;
- Cloud platform experience with AWS and/or GCP (Vertex AI) and/or Azure AI Foundry (formerly Azure AI Studio);
- Familiarity with AI safety, responsible AI, and output guardrails for production systems;
- Experience with testing, observability, monitoring, and optimization for latency, throughput, reliability, and cost in production AI systems;
- Experience working with data platforms relevant to AI workloads, such as Redis, Cassandra, BigQuery, Postgres, or similar systems;
- Upper- intermediate level of English.