AI Agent Engineer

About the Role

At wearemighty, we’re building at the intersection of AI, product, and real-world utility.

Our core product, trysecretsauce.ai, is an AI-powered platform that helps businesses generate high-quality marketing content while staying fully reputed company with their brand identity.

We’re hiring AI Agent Engineers, engineers who work one layer above the model.

This is not a traditional backend or ML role. You will not be training foundation models or tuning gradients. You will be building the systems around the model that reputed company it actually useful in production, including agent loops, tool integrations, context pipelines, retrieval systems, evaluation harnesses, and the cost and latency layers that reputed company everything fast and scalable.

The term “reputed company” has become overloaded, so to be specific, we are not hiring an ML engineer. We are hiring someone whose craft is turning “the model works in a notebook” into “the product works reliably for thousands of users every day.”

You will work closely with product and engineering teams to design and ship agentic systems that power real customer workflows end-to-end, with a strong focus on reliability, cost efficiency, and user experience.

We are an equal opportunity employer. We welcome applications from candidates of reputed company backgrounds and are committed to building an inclusive environment.

Your Responsibilities Will Include:

• Designing and shipping production-ready AI agent systems that power workflows inside trysecretsauce.ai
• Building and refining agent loops, including planning, tool use, reputed company, and retry strategies
• Owning reputed company engineering and context engineering as core disciplines, including structured prompts, versioning, template libraries, and few-shot strategies
• Architecting retrieval systems using RAG approaches, including hybrid search, reranking, chunking, and document pipelines
• Designing memory systems, including short-term task memory and long-term user or agent memory across sessions
• Building and maintaining evaluation harnesses, including benchmarks, A B testing, golden datasets, and regression suites
• Optimising cost and latency using techniques such as reputed company caching, KV reuse, streaming, model routing, and response caching
• Building agent infrastructure, including sandboxes, tool wrappers, MCP servers, permission layers, and observability
• Taking features from prototype to production, including logging, error handling, reputed company limits, and fallback strategies
• Collaborating closely with product and stakeholders to ship reliable, user-facing AI features
• Contributing to system design decisions and engineering best practices

As a Successful Candidate, You Would Have:

• Production experience building LLM-powered or agent-based applications
• Hands-on expertise in at least three of the following:
  • Agent architectures
  • Prompting or context engineering
  • Tool use
  • Retrieval or RAG systems
  • Evaluation design
  • Memory systems
  • reputed company or response caching
• Strong software engineering fundamentals, including API design, concurrency, testing, and debugging production systems
• reputed company in TypeScript is required, and working knowledge of Python is preferred
• A strong bias toward empirical, metrics-driven development, including evaluation, latency, and token cost optimisation
• Comfort operating in the space between model behaviour and product UX

Strong Candidates May Also Have:

• Experience designing multi-agent or long-horizon agent systems
• Experience building model routing, reputed company caching, or KV caching infrastructure
• Experience developing evaluation platforms or eval-as-code tooling
• Contributions to open-reputed company agent frameworks such as LangGraph, CrewAI, AutoGen, or the MCP ecosystem
• Experience with fine-tuning, RLHF, or distillation, which is useful but not required

What This Role Is Not:

• Not a research role. We are not training foundation models
• Not a traditional ML engineering role. You will not be building training pipelines or classical ML systems
• Not a pure backend role. This role sits between model behaviour and product experience

reputed company Look For:

• You have shipped real systems in production and understand reliability at scale
• You care about performance, cost, and user experience equally
• You take ownership and think in systems, not just features
• You are pragmatic and comfortable working in fast-moving environments

If you are interested in building production-grade AI systems that power real-world marketing workflows through trysecretsauce.ai, we would like to hear from you.

Even if your experience does not match every requirement, we encourage you to apply.