GenAI Engineer

You might be our missing piece if you have:

Strong Python skills and a solid software engineering foundation.

Experience building production-grade backend applications with FastAPI, Flask, or Django.

Hands-on experience designing, building, and deploying ML and/or GenAI solutions in production.

Experience with GenAI frameworks such as LangChain, LangGraph, ADK, or Haystack.

A good understanding of commercial LLM APIs and how to use them effectively in real products.

Strong experience with RAG systems, including embeddings, vector search, retrieval pipelines, chunking, reranking, and context construction.

Experience working with agentic systems, tool use, orchestration flows, and multi-step execution.

Familiarity with MCP and its role in connecting agents with tools, data sources, and external systems.

Experience implementing evaluation strategies for GenAI systems, including quality, latency, cost, and hallucination tracking.

Comfort working with relational/non-relational databases, vector stores, and data pipelines for ML or GenAI use cases.

Familiarity with Docker, containerized workflows, and MLOps practices such as CI/CD, experiment tracking, and model versioning.

Experience building observable systems, with solid logging, monitoring, and debugging practices.

Good judgment around privacy, security, and guardrails for AI systems.

An interest in agentic coding and spec-driven development.

Experience taking AI/ML work from exploration to production in a team setting.

A collaborative mindset and willingness to support junior colleagues through reviews, pairing, and knowledge sharing.

We would be thrilled if you have:

Experience evaluating and improving agentic systems in real-world settings.

Hands-on experience with MCP-based integrations.

Familiarity with prompt engineering and context engineering.

Exposure to research and the ability to turn ideas into practical solutions.

Comfort with cloud infrastructure such as AWS, Azure, GCP, or DigitalOcean.

Hands-on experience with Databricks or Apache Spark.

Experience with tooling for LLM observability or evaluation.

Familiarity with authentication, authorization, and data governance in enterprise environments.

Experience contributing to technical communities, talks, or internal documentation.

We will be working together on the following:

Understanding client needs and helping shape practical AI solutions.

Designing and delivering agentic RAG systems that are reliable and production-ready.

Building integrations between agents, tools, and external systems, including through MCP where relevant.

Owning AI/ML components from prototyping through deployment, monitoring, and iteration.

Applying evaluation approaches for retrieval, generation, and agent behavior.

Contributing to good engineering practices around reproducibility, observability, scalability, and reliability.

Working closely with product, platform, and infrastructure teams.

Troubleshooting production issues and improving system reliability over time.

Supporting the team through code reviews, pair programming, and shared learning.

Staying close to industry developments and applying what is useful in practice.