About TechTorch

TechTorch is a high-growth enterprise technology consultancy that partners with the world’s leading private equity-backed businesses. We deliver AI-powered solutions, accelerators, and data-driven transformation initiatives that drive measurable value at speed and scale.

Our mission is to redefine enterprise technology consulting for private equity. We combine the agility of a scale-up with the discipline and rigor demanded by the most sophisticated investors and operators.

TechTorch was founded by seasoned leaders — including former Bain consultants, CIOs, and tech executives — with deep expertise in technology, transformation, and value creation. We are not a typical startup. We were built to succeed and to deliver results that matter.

About the Team

TechTorch teams are at the forefront of delivering enterprise technology solutions that help our clients transform their operations, unlock growth, and achieve their investment theses. We combine deep product and domain expertise with a business-first mindset, enabling us to design and deliver solutions that align with client priorities and timelines.

Our teams operate globally, collaborating across disciplines — from AI and data to enterprise systems and digital transformation — to deliver high-impact outcomes.

Role overview

We’re looking for an AI Software Developer 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.

What you’ll do

• 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

Qualifications

• 2–3+ 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