AI Engineer developing advanced AI and computer vision systems for industrial automation at Synergeticon. Leveraging cutting - edge research to create production - ready solutions.
About the role
- AI Engineer designing and delivering GenAI solutions at RebelDot. Collaborating across teams to build reliable systems and improve client AI offerings.
Responsibilities
- 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.
Requirements
- 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.
Benefits
- Professional development opportunities
- Flexible working hours
Job title
Job type
Experience level
Salary
Not specified
Degree requirement
Tech skills
Location requirements
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