Founding AI Engineer

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Transform+

Founding AI Engineer

1. End‑to‑End Model Build & Deployment

• Architect, train, and fine‑tune multilingual speech‑to‑text, diarisation, NER, summarisation, and scoring models (Whisper, wav2vec 2.0, transformer‑based NLP).
• Design RAG pipelines and prompt‑engineering frameworks with commercial and open‑source LLMs (OpenAI, Mistral, Llama 2).
• Build GPU/CPU‑optimised inference micro‑services in

  Python/FastAPI

  with strict latency budgets.

1. Production Engineering

• Implement asynchronous processing, message queues, caching, and load balancing for high‑concurrency voice and text streams.
• Establish CI/CD, model versioning, A/B testing, and automated rollback for ML APIs.

1. Data Strategy & Tooling

• Define data‑collection, labelling, and active‑learning loops; build pipelines for continuous model improvement.
• Create evaluation harnesses (WER, ROUGE, AUROC, latency) and automate nightly regression tests.

1. Security & Compliance

• Implement role‑based access, encryption‑at‑rest/in‑transit, and audit logging for all AI endpoints.
• Ensure adherence to enterprise infosec requirements and regional data‑privacy standards.

1. Cross‑Functional Collaboration

• Partner with product managers to translate customer pain points into technical requirements and success metrics.
• Work with backend, DevOps, and frontend teams to expose AI outputs via dashboards, APIs, and real‑time agent assist overlays.

1. Technical Leadership

• Establish coding standards, documentation templates, and peer‑review culture for the AI team.
• Mentor junior engineers as the team scales; influence hiring and tech‑stack decisions.

• 4 – 8 years building and deploying ML systems in production (audio, NLP, or LLM focus).
• Expert‑level Python; strong grasp of PyTorch (or TensorFlow), Hugging Face Transformers, and data‑processing libraries.
• Proven record of optimising inference pipelines for sub‑second latency at scale.
• Hands‑on experience with cloud infrastructure (AWS or GCP), Docker/Kubernetes, and CI/CD for ML.
• Deep understanding of REST/gRPC APIs, security best practices, and high‑availability architectures.
• Ability to articulate trade‑offs and align technical decisions with business outcomes.

• Prior work on Indic or Arabic speech/NLP, code‑switching, or low‑resource language modelling.
• Familiarity with vector databases (Pinecone, FAISS), Redis Streams/Kafka, and GPU orchestration (Triton, TorchServe).
• Exposure to sales‑tech, call‑centre analytics, or real‑time coaching platforms.
• Contributions to open‑source AI projects or relevant peer‑reviewed publications.

• ≥ 25 % reduction in transcription error rate or latency across core languages.
• Two net‑new AI modules shipped to production and adopted by Tier‑1 clients.
• Robust CI/CD and monitoring pipelines in place with < 1 % model downtime.
• Documentation and onboarding playbooks enabling AI team headcount to double without quality loss.

• A builder who takes ideas from whiteboard to production with minimal supervision.
• A systems thinker who balances algorithmic innovation with engineering pragmatism.
• A hands‑on leader who codes, mentors, and sets the technical bar through example.
• A product‑centric technologist who obsesses over user impact, not benchmark vanity.
• A lifelong learner who follows the bleeding edge of GenAI and applies it wisely.

careers@darwix.ai

Founding AI Engineer