About the Role :

The AI Forward Deployed Engineer works directly with clients to turn frontier AI capabilities into production systems that solve real business problems. The role sits at the intersection of software engineering, applied AI, and client delivery. The engineer will build custom AI applications and agentic workflows, and will integrate and extend platforms from the firm's technology partners, including autonomous AI software engineering agents, computer-use and web-automation agents, and open-weight large language models and agent frameworks.

This is a hands-on, customer-facing engineering role. The engineer is expected to scope problems with clients, prototype quickly, ship to production, and own outcomes end to end.

Key Responsibilities :

- Design, build, and deploy AI-powered applications and agentic workflows for client use cases across legal, finance, private equity, and software engineering.

- Integrate, configure, and extend partner AI platforms (autonomous coding agents, computer-use/browser agents, and open-weight LLM agent frameworks) within client environments.

- Build retrieval-augmented generation (RAG) pipelines, knowledge graphs, and semantic layers over client data, documents, and business systems.

- Develop tool integrations and connectors (APIs, MCP servers, databases, SaaS systems such as Slack, Google Workspace, CRMs) that allow agents to act on real systems.

- Implement evaluation frameworks, guardrails, observability, and human-in-the-loop controls to ensure accuracy, safety, and reliability of AI outputs.

- Run discovery sessions with client stakeholders, translate business requirements into technical designs, and present demos and progress updates.

- Rapidly prototype proofs of concept, then harden them into secure, maintainable, production-grade systems.

- Help client engineering teams adopt AI-assisted software development practices and tooling to improve productivity, quality, and delivery speed.

- Document architectures, decisions, and runbooks; contribute reusable components and playbooks back to the firm.

- Provide structured feedback to partner product teams on gaps, bugs, and feature needs observed in the field.

Required Skills and Experience :

- 3+ years of professional software engineering experience, with at least 1 - 2 years building applications on top of LLMs.

- Strong proficiency in Python and/or TypeScript/Node.js; comfortable across backend, APIs, and lightweight front-end work.

- Hands-on experience with LLM APIs (e.g., Anthropic, OpenAI, or open-weight models) including prompt engineering, tool/function calling, structured outputs, and context management.

- Experience building agentic systems: multi-step agents, tool use, planning, memory, and orchestration frameworks (e.g., LangGraph, Agent SDKs, or custom harnesses).

- Practical experience with RAG: embeddings, vector databases (e.g., pgvector, Pinecone, Weaviate), chunking strategies, and retrieval evaluation.

- Working knowledge of the Model Context Protocol (MCP) or comparable approaches to connecting agents with external tools and data.

- Daily, fluent use of AI coding assistants and autonomous coding agents as part of the development workflow.

- Solid foundation in cloud infrastructure (AWS, GCP, or Azure), Docker, CI/CD, and Git-based workflows.

- Experience with SQL and relational databases; ability to work with messy, real-world enterprise data.

- Understanding of LLM evaluation, cost/latency trade-offs, and security considerations (data privacy, prompt injection, access control).

- Excellent written and spoken English; able to communicate clearly with senior, non-technical client stakeholders.

- Strong ownership mindset and ability to work independently in ambiguous, fast-moving client environments.

Preferred Qualifications :

- Prior experience in a forward deployed, solutions engineering, consulting, or client-facing engineering role.

- Experience with browser automation and computer-use agents (e.g., Playwright, Selenium, or vision-based UI agents).

- Experience deploying, fine-tuning, or serving open-weight models (e.g., vLLM, Ollama, Hugging Face ecosystem).

- Exposure to knowledge graphs, ontologies, or semantic layers.

- Domain familiarity with legal, financial services, private equity, or B2B SaaS.

- Experience with Slack, Google Workspace, Salesforce, HubSpot, or similar enterprise integrations.

- Public portfolio of AI projects (GitHub, demos, technical writing, or open-source contributions).

Working Hours :

- This is a remote role supporting US-based clients.

- The engineer must work at least 4 hours per day overlapping with US business hours (Eastern/Central time), for example 6:30 PM - 10:30 PM IST, with the remainder of the schedule flexible.

- Occasional availability for later client meetings or production releases is expected.

What Top of Mind Labs Offers :

- Direct work with US clients, including private equity firms and high-growth technology companies.

- Hands-on access to frontier AI platforms through the firm's technology partnerships.

- High ownership and visibility, working closely with firm leadership.

- Competitive compensation and fully remote work.

- Rapid learning and growth in one of the fastest-moving areas of software engineering.