Required skills

Area

Requirement

Languages

Python at expert level. TypeScript or JavaScript at working level.

ML stack

PyTorch, Hugging Face Transformers, TRL, PEFT, Accelerate, Datasets

Training

SFT, LoRA and QLoRA, distributed training, dataset curation

Alignment

Hands-on work in at least one of RLHF, DPO, GRPO, or RLVR, with measured results

Agents

Tool calling, MCP, agent loops, context management, agent evaluation

Inference vLLM, TGI, or llama.cpp. Quantisation and latency tuning.

Backend

FastAPI, async Python, Pydantic, PostgreSQL, Redis

Data

Embeddings, vector databases, RAG pipelines, chunking strategy

Operations

Docker, Git, Linux, CI/CD, GPU basics (CUDA, VRAM planning)

Evaluation

Benchmarks, custom eval sets, LLM as judge, regression tests

Positive to have

- Federated learning, differential privacy, or other privacy preserving training methods.

- Custom CUDA or Triton kernels.

- Model distillation and model merging.

- Guardrails, red teaming, and jailbreak testing.

- Multimodal models: vision, speech, or document understanding.

- Open source contributions to any part of this stack.

- Experience with on-premise or air-gapped deployment.

📌 AI Full Stack Developer (Chennai)
🏢 Automios
📍 Chennai