AI Software Developer @ TechTorch

Minimum qualifications 1–2+ years of hands-on experience building LLM-powered agents or complex automations Strong Python skills, including async programming, testing, and packaging Experience with TypeScript (in addition to Python) is a plus, as it’s an emerging language in our stack Practical experience with LangGraph and/or LangChain, plus one or more of: n8n, AWS Bedrock, Airflow, Prefect, Temporal, or similar orchestration tools Working knowledge of core model ecosystems: Gemini, OpenAI, and Claude (prompting, tool use, structured output, function calling) Solid data foundations: vector stores, embeddings, RAG patterns, chunking, indexing, and data quality Software engineering fundamentals: Git, code reviews, CI, containerization, cloud services (AWS preferred) Familiarity with ADK (Agent Development Kit) or equivalent frameworks for building and standardizing agent behaviors Nice to have Experience with agents in production: evaluation frameworks, red-teaming, safety filters, rate-limit and cost controls Retrieval and data management at scale: document processing, ETL, and governance AI orchestration with frameworks and runtimes such as LangGraph, LangChain, n8n, AWS Bedrock, Azure Foundry, and similar tools Backend development: FastAPI, Flask, Node.js, or Go; relational and NoSQL databases (e.g., Postgres, MongoDB) and modern platforms like Supabase Workflow automation with third-party apps and enterprise systems (Slack, Google Workspace, Salesforce, ServiceNow, etc.) Observability stacks: OpenTelemetry, Langfuse, Weights & Biases, or custom analytics About TechTorch TechTorch is building the future of intelligent work. We help clients design, build, and deploy AI agents that automate complex, real-world workflows - delivering both reliability and measurable ROI. As part of our team, you won’t just be experimenting with ideas; you’ll be shipping production systems that create real impact across multiple industries. You’ll join a hands-on, fast-moving group that thrives on solving hard problems, pushing the boundaries of what AI can do, and turning breakthrough technology into practical results. Role overview We’re looking for an AI Engineer to design, implement, and maintain agentic systems for our clients. You’ll work across LLMs, orchestration frameworks, and data pipelines to deliver robust, observable, and secure automations. This is not a research-only role, and you won’t be stuck in endless proof-of-concepts. Every project you take on will target production and client systems, with real use cases and measurable impact. You’ll be joining a hands-on team that thrives on solving challenging problems, shipping production-grade systems, and pushing the boundaries of what AI can do in real business environments. How you work Systems thinker who can break down ambiguous client problems into shippable agent capabilities Bias to action, comfort with rapid experimentation and clear measurement Strong written communication and partner‑facing collaboration skills Location and working model Remote‑first with occasional client or team onsite sessions as needed Benefits and compensation Competitive salary Flexible time off and remote work support Professional development budget for conferences, certifications, and experimentation If you’re excited to build dependable AI agents that solve real problems, we’d love to talk. ,[Design and implement agent architectures and multi-step orchestrations in Python, Build with frameworks and runtimes such as LangGraph, LangChain, n8n, AWS Bedrock, and similar tools, Integrate and evaluate frontier models, e.g., Google Gemini, OpenAI GPT family, and Anthropic Claude, Develop connectors to SaaS and enterprise systems via APIs, webhooks, queues, and events, Implement tools, memory, retrieval, and planning strategies for reliable agent behavior, Author a reference agent template / internal SDK (LangGraph patterns, tool adapters, memory/plan primitives) adopted across agents, Productionize prototypes: CI/CD, packaging, containerization, and deployment to cloud, Instrument agents for observability: logging, tracing, evaluation, offline analysis, and guardrails, Collaborate with client teams to capture requirements, run experiments, and iterate quickly, Facilitate design thinking workshops to understand business requirements, map user journeys, and translate needs into agent capabilities and success metrics, Apply security, privacy, and safety best practices for data handling and model usage] Requirements: n8n, AWS Bedrock, Prefect, Temporal, Gemini, OpenAI, Claude, RAG, Testing, Python, TypeScript, AWS, Airflow, Git, Cloud, Data management, ETL, AI, Azure, FastAPI, Flask, Node.js, Go, NoSQL, PostgreSQL, MongoDB, Slack, Google Workspace, Salesforce