Datacolor is an AI engineering company helping enterprises move from AI experimentation to production AI. We work directly with CIOs, CDOs, and business leaders at Fortune 500 organizations to solve real business problems using Agentic AI, trusted data, automation, and modern enterprise platforms.
You'll work alongside experienced founders, enterprise architects, AI engineers, and technology leaders who have built companies and delivered large-scale enterprise transformations.
You'll help shape solutions, translate emerging AI capabilities into enterprise applications, and gain exposure to solution architecture, deployment, evaluation, and customer-facing problem solving.
While models and tools will evolve, the ability to design trusted AI systems, work with enterprise data, and deliver measurable business outcomes will remain highly valuable. Our goal is to help develop the next generation of AI builders, architects, and technology leaders.
- Design and build LLM agent and multi-agent systems covering planning, tool use, orchestration, memory, and validation.
- Architect enterprise RAG pipelines over structured and unstructured data using vector databases (e.g., Weaviate, Pinecone, pgvector).
- Develop AI automation pipelines — workflow engines, decision agents, and data-quality agents — that integrate with business systems.
- Build and maintain Python backends (FastAPI, async services) powering data-heavy AI applications.
- Implement LLM evaluation frameworks, guardrails, and benchmarks to ensure reliability and safety in production.
- Drive token/cost optimisation: cost tracking, budget enforcement, and model selection heuristics.
- Build MCP-compatible tools and agent integrations across providers (OpenAI, Anthropic, Gemini).
- Own CI/CD and containerised deployment of AI services (Docker, GitHub Actions).
- Mentor engineers, share technical deep-dives, and contribute to internal and open-source AI tooling.
- 7–10 years of overall software engineering experience, with strong Python backend skills.
- 2–3 years of hands-on experience building AI/GenAI systems in production.
- Practical experience with LLM frameworks such as LangChain or LlamaIndex, and agentic architectures.
- Hands-on experience building RAG systems with vector databases and embedding pipelines.
- Experience integrating LLM APIs (OpenAI, Gemini, Anthropic) including multimodal pipelines (text, vision, structured data).
- Solid grounding in FastAPI (or similar), async programming, and API design.
- Familiarity with Docker, Git, and CI/CD workflows (e.g., GitHub Actions).
- Strong understanding of LLM evaluation, prompt engineering, and cost/performance trade-offs.
- Open-source contributions to AI frameworks (LangChain ecosystem, vector DB integrations, etc.).
- Published technical writing, packages (PyPI), or public speaking/lectures on LLMs, RAG, or agents.
- Experience with MCP (Model Context Protocol) tooling and agent interoperability standards.
- Exposure to cloud AI platforms (GCP Vertex AI, AWS Bedrock, Azure OpenAI).
- Working knowledge of JavaScript/TypeScript for full-stack AI product work.
- Competitive compensation
- Opportunity to ship enterprise GenAI products end-to-end with a high-ownership team.
- Learning budget, conference support, and encouragement for open-source contributions.