About Redesign Group

The Redesign Group is a global technology and cybersecurity Solution Provider leveraging design thinking, interdependent subject matter expertise, and emerging technology solutions to help organizations achieve meaningful transformation. Our globally diverse team members, partnerships with strategic technology manufacturers, and cybersecurity services position us to be a business partner that’s ready for what’s next.

We live by our core values of operating in the service of others, being problem solvers, and focusing on long term partnerships. You’ll excel at Redesign if you thrive in a rewarding environment that moves quickly, challenges you to grow and fosters collaboration. We seek candidates with a hands-on customer-first approach, robust interpersonal and communication skills, strong work ethic and excellent time management. While teamwork is expected, the ability to work independently in a fast-paced environment is crucial.

Job Description
We’re hiring an exceptional Senior Data Science Engineer to own end-to-end LLM-powered features — not a scissors-and-paste prompt engineer. You’ll design, ship, and operate robust LLM systems (RAG, agents, fine-tuning, inference optimization) that meet product SLAs, reduce risk, and deliver measurable business impact. We want deep ML/engineering judgment, product thinking, and ownership — tools are just a means to an end.

What you’ll own

• Architect, implement, and run production LLM systems (retrieval pipelines, agent orchestrations, fine-tuning flows) from prototype to scale.
• Translate ambiguous product problems into measurable ML objectives and choose pragmatic tradeoffs (model size vs latency, retrieval strategy vs accuracy, cost vs quality).
• Build evaluation pipelines (automated + human), define KPIs for hallucination, relevance, fairness, and monitor drift + performance in production.
• Optimize inference and deployments (quantization, batching, autoscaling, cache strategies)

Must-have

• 5+ years in ML/Deep Learning engineering or applied research with production deployments.
• Deep understanding of transformer models and training/fine-tuning techniques (including parameter-efficient methods like LoRA/adapters).
• Hands-on experience building RAG pipelines and/or agentic workflows with attention to retrieval quality and context engineering.
• Production experience with inference optimization and serving (latency/cost tradeoffs, quantization, batching, autoscaling).
• Strong software engineering skills: production code, testing, CI/CD, observability, and clear documentation.
• Clear communicator who can explain tradeoffs to product, infra, and business stakeholders.

Nice-to-have

• Experience with vector DBs (Qdrant ..) and embedding architectures.
• Familiarity with inference engines (vLLM, Triton, HF Inference) or model compression/distillation.
• Background in ML safety, governance, or compliance auditing.
• Experience designing human-eval frameworks and running A/B tests for model versions.

Tech stack
Python, PyTorch / Transformers ecosystem, vector DBs (Qdrant or other), RAG frameworks (LangChain/LlamaIndex), inference/serving tools (vLLM, Triton, HF endpoints), containerization (Docker), monitoring (Prometheus/Grafana, custom model metrics). Note: we value conceptual fluency over specific brand loyalty.