AI Engineer — Training

1 week ago

Bengaluru, India eeKee AI Full time

Company Description

Eekee AI is an AI-driven life coach designed to help employees feel grounded at work and build a lasting sense of purpose. Rooted in Ikigai and Viktor Frankl’s logotherapy, Eekee uses daily conversations and psychometric signals to identify strengths and values, suggesting resources like books, courses, and team rituals. Eekee supports HR teams by improving morale, reducing burnout, and enhancing one-to-one conversations in a private, controlled space. Integrated with Slack and Microsoft Teams, Eekee provides continuous support through thoughtful questions and resource suggestions.


Role Description

Own training, alignment, and deployment of our meaning-at-work coach. You’ll design data pipelines, fine-tune models, build safety layers, set evals, and ship to production.


What You’ll Do
  • Build data flow: sourcing, cleaning, consent/PII scrubbing, augmentation, labeling guidelines
  • Fine-tune/align (SFT, DPO/RLHF) for coach behavior: listen → reflect → one powerful question → next step
  • Implement safety: crisis/HR escalation classifiers, toxicity/harassment filters, privacy guardrails
  • Add context: retrieval for work scenarios; prompts/tools for “Question of the Day” and “Vent”
  • Create evals: conversation quality, protocol adherence, safety, bias, latency, cost
  • Optimize inference: distillation, quantization, caching, batching; observable, canaried rollouts
  • Document: model cards, red-team results, alignment notes


Minimum Qualifications
  • 1+ years applied ML/LLMs; shipped fine-tuned models
  • Strong in PyTorch/JAX and serving (vLLM/Triton/Ray/K8s)
  • Experience with SFT/DPO/RLHF, synthetic data, eval harnesses
  • Built safety/quality classifiers and RAG systems
  • Pragmatic on latency/cost; solid profiling chops
  • Clear technical writing
Nice to Have
  • Coaching/org-psych or agent-design background
  • On-device/edge inference; multilingual safety
  • Model cards, red-teaming, compliance awareness
  • Zero-to-one startup experience
Stack

Models: OpenAI/Anthropic, Llama/Mistral, LoRA/QLoRA, GGUF

Tooling: PyTorch, HF, vLLM, Triton, Ray, W&B

RAG: embeddings + FAISS/pgvector, guardrails

Infra: K8s, cloud GPUs, Terraform; analytics + experiment tracking

30/60/90 Day goals
  • 30: Offline eval harness; baseline fine-tune matching coach style; first safety classifiers; model card v0
  • 60: Prod “daily question” + “vent” flows w/ guardrails; cut token cost by 30–50%; A/B live
  • 90: Distilled model in prod; red-team playbook; documented alignment/escalation; +20% conv-quality score


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