Memologs is the marketing decision platform — the only one with a memory. We work with marketing teams spending $100K+ per month on media across fintech, D2C, retail, consumer technology and health. Advertising platforms overcount conversions by approximately 3×, creative fatigue is typically identified 11–18 days late, media mix reports arrive around 98 days after the decisions they were intended to inform, and institutional knowledge is lost when team members leave. Memologs addresses all four.

We are hiring an AI/ML Engineer to own the model layer of the platform.

Responsibilities

- Build and maintain embedding pipelines across image, video and text data to support similarity measurement between creatives and between historical decisions.
- Train and fine-tune models on proprietary data, including assessing whether the available data is sufficient to support training.
- Develop supervised models that produce forecasts with quantified uncertainty rather than point estimates.
- Design offline evaluation frameworks for problems without pre-existing labels, covering corpus construction, metric selection and validation of the evaluation itself.
- Integrate large language models where appropriate, with grounding and validation controls that prevent generated output from asserting figures not present in the source data.
- Deploy models to production, including versioning, shadow testing against the incumbent model, and drift monitoring.
- Manage inference cost and latency alongside model accuracy as design constraints.
- Build video and audio processing pipelines covering keyframe extraction, scene detection and transcription, with graceful degradation on malformed or unavailable media.

Requirements





- 3+ years of professional experience delivering machine learning or applied AI systems in production environments.
- Strong Python, with the ability to work within a standard application codebase including services, background jobs and database work.
- Practical experience with PyTorch and the Hugging Face ecosystem, covering model loading, fine-tuning, batching and serving.
- Demonstrated experience fine-tuning models and evaluating them rigorously, including correct train and test separation and selection of metrics aligned to the business decision.
- Working knowledge of embeddings and vector search in production, including dimensionality, similarity behaviour and version management of stored vectors.
- Sound judgement across both classical machine learning and deep learning, including when a smaller supervised model is preferable on cost, latency or explainability grounds.
- Practical understanding of large language model failure modes and mitigation techniques, including grounding, structured output, refusal handling, and the limitations of model-based evaluation.
- Experience treating inference cost and latency as first-class design constraints.

Preferred

- Multimodal or vision-language models, video understanding, or speech-to-text.
- LoRA or other parameter-productive fine-tuning methods for larger models.
- Deployment to a managed ML platform (Vertex AI, SageMaker, Cloud Run) and experience with model registries or experiment tracking.
- Multi-tenant machine learning, including cold-start handling for customers with no historical data.
- Exposure to experimentation or causal inference. This is not owned by the role, but an understanding of what measured results do and do not support is valuable.

Pay: ₹413,643.11 - ₹1,706,294.96 per year

Location:

- Delhi (Required)

Work Location: Hybrid remote in Delhi

📌 AI/ML Engineer (Delhi)
🏢 MemoLogs
📍 Delhi