Applied AI Engineer- Generative & Cognitive Technologies

text, vision, and multimodal

Model Engineering

• Fine-tune and quantise open-weight LLMs (Llama 3, Mistral, Gemma) and SLMs for low-latency edge inference.

• Train or adapt computer-vision models (YOLO, Segment Anything, SAM-DINO) to detect site hazards, drawings anomalies, or asset states.

Multimodal Pipelines

• Build retrieval-augmented-generation (RAG) stacks: loaders → vector DB (FAISS / OpenSearch) → ranking prompts.

• Combine vision + language outputs into single “scene → story” responses for dashboards and WhatsApp bots.

Serving & MLOps

• Package models as Docker images, SageMaker endpoints, or ONNX edge bundles; expose FastAPI/GRPC handlers with auth, rate-limit, telemetry.

• Automate CI/CD: GitHub Actions → Terraform → blue-green deploys.

Evaluation & Guardrails

• Design automatic eval harnesses (BLEU, BERTScore, CLIP similarity, toxicity & bias checks).

• Monitor drift, hallucination, latency; implement rollback triggers.

Enablement & Storytelling

• Write prompt playbooks & model cards so other teams can reuse your work.

• Run internal workshops: “From design drawing to narrative” / “LLM safety by example”.

• 3+ yrs ML/NLP/CV in production; at least 1 yr hands-on with

  Generative AI

  .
• Strong Python (FastAPI, Pydantic, asyncio) and

  HuggingFace Transformers

  OR

  diffusers

  .
• Experience with minima­l-footprint models (LoRA, QLoRA, GGUF, INT-4) and vector search.
• Comfortable on AWS/GCP/Azure for GPU instances, serverless endpoints, IaC.
• Solid grasp of evaluation/guardrail frameworks (Helm, PromptLayer, Guardrails-AI, Triton metrics).

• Built a RAG or function-calling agent used by 500+ users.
• Prior CV pipeline (object-detection, segmentation) or speech-to-text real-time project.
• Live examples of creative prompt engineering or story-generation.
• Familiarity with LangChain, LlamaIndex, or BentoML.

• Multidomain playground

  – text, vision, storytelling, decision-support.

• Tech freedom

  – pick the right model & stack; justify it; ship it.

• Remote-first

  – work anywhere ±4 hrs of IST; quarterly hack-weeks in Hyderabad.

• Top-quartile pay

  – base + milestone bonus + conference stipend.

1. Send a resume and link to GitHub / HF / Kaggle showcasing LLM or CV work.
2. Include a 200-word note describing your favourite prompt or model tweak and the impact it had.
3. Short-listed candidates complete a practical take-home (fine-tune tiny model, build RAG or vision demo, brief write-up) and a 45-min technical chat.

We hire builders, not resume keywords. Show us you can ship AI that works in the real world—and explain it clearly—and you’re in.