Founding AI Engineer

Location: Bengaluru

The Role

You'll be the architect and owner of Neo's AI infrastructure. This means training custom models for our unique use cases, building production ML pipelines, and creating the reasoning systems that make Neo intelligent. You'll work across the full ML lifecycle - from data pipelines to model training to production deployment.

What You'll Own

1. Custom Model Development & Training

Build specialized models that foundation models can't provide. Train speaker diarization for Indian accents, fine-tune embedding models for conversational memory, develop custom NER for Hindi-English code-mixing, and optimize models for edge deployment.

Key Challenges:

● Train speaker diarization models on Indian multi-speaker conversations with code-mixing

● Fine-tune embedding models for semantic search across temporal context

● Build custom NER/entity linking for Hindi-English mixed conversations

● Optimize transformer models for mobile deployment with

● Handle class imbalance in emotion detection and intent classification

Tech Stack:
PyTorch/Tensor Flow for model training, Hugging Face for fine-tuning, ONNX/Tensor

RT for optimization

2. Memory Architecture & ML Pipeline

Build the brain that remembers everything. Design temporal knowledge graphs that ingest conversations, extract entities and relationships using custom-trained models, and enable longitudinal pattern detection. Own the full ML pipeline from data ingestion to model inference to graph updates.

Key Challenges:

● Bi-temporal data models with real-time updates

● Entity linking across noisy conversational transcripts

● Relationship extraction using fine-tuned sequence models

● Pattern detection with unsupervised learning (clustering, anomaly detection)

● Privacy-preserving embeddings and federated learning

Tech Stack:
PyTorch for custom models, Neo4j/graph databases, vector databases (Qdrant), streaming pipelines

3. Audio Processing & Speech ML

Own the end-to-end speech pipeline. Train/fine-tune ASR models for Indian languages, build speaker diarization systems, develop audio quality assessment models, and optimize for edge deployment. Handle the unique challenges of Indian conversational speech.

Key Challenges:

● Fine-tune Whisper/wav2vec2 for 15+ Indian languages with code-mixing

● Train speaker diarization models handling overlapping speech

● Build voice activity detection for noisy environments

● Develop audio quality assessment using CNNs

● Optimize models for real-time mobile inference (quantization, pruning)

Tech Stack:
PyTorch, Torch Audio, Kaldi, ESPnet, model compression techniques

4. Intelligence & Reasoning Layer

Create the query understanding and reasoning system. Build hybrid retrieval combining dense embeddings with graph traversal, train ranking models for result quality, develop proactive insight detection, and fine-tune LLMs for conversational queries.

Key Challenges:

● Train re-ranking models for temporal query results

● Fine-tune LLMs for Hindi-English conversational queries

● Build classification models for query intent and temporal scope

● Develop anomaly detection for proactive insights

● Handle distribution shift as user behavior evolves

Tech Stack:
PyTorch, sentence-transformers, LLM fine-tuning (LoRA, QLoRA), scikit-learn

5. Multi-Agent Systems & Orchestration

Design agent orchestration where specialized AI agents collaborate. Train classifier models for routing queries, build reward models for agent evaluation, develop action prediction models, and create meta-learning systems that improve over time.

Key Challenges:

● Train intent classification for agent routing

● Build RL-based systems for multi-step action planning

● Develop evaluation models for agent output quality

● Create meta-learning pipelines for continuous improvement

● Handle conflicting agent recommendations with trained arbitration models

Tech Stack:
PyTorch, Ray for distributed training, custom RL implementations

6. Neo Core SDK & ML Infrastructure

Build enterprise ML APIs with custom model serving. Design multi-tenant architecture with model versioning, build A/B testing infrastructure, implement model monitoring and drift detection, and create auto-scaling inference pipelines.

Key Challenges:

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