About the Role

We are seeking an experienced Data Scientist to lead innovation in document intelligence, OCR, and agentic AI systems. You will design and fine-tune large language models (LLMs) and multimodal pipelines to parse, structure, and reason over complex engineering documents. Your work will directly impact safety-critical workflows, enabling faster, more accurate decision-making in aerospace and manufacturing contexts. Engineering Stress Dossiers, Engineering Documents.

Key Responsibilities

- Architect and deploy agentic AI pipelines for document parsing, stress dossier intelligence, and multimodal reasoning.
- Develop and productionise OCR workflows (multilingual, mixed-script, layout-aware) using frameworks like Tesseract, EasyOCR, LayoutLM, and custom PyTorch pipelines.
- Fine-tune LLMs and vision-language models (VLMs) for structured extraction, semantic search, and citation-grounded reasoning.
- Implement Human-in-the-Loop (HITL) validation workflows to ensure accuracy, traceability, and continuous model improvement.
- Collaborate with engineering teams to integrate models into production APIs, secure on-prem/cloud deployments, and enterprise data pipelines.




- Conduct exploratory data analysis and communicate findings to technical and non-technical stakeholders.
- Define KPIs, run A/B experiments, and rigorously evaluate model performance.

Required Qualifications

- 58 years of hands-on experience in ML, computer vision, or document AI.
- Robust proficiency in Python (NumPy, Pandas, Scikit-learn, PyTorch/TensorFlow).
- Expertise in OCR and document parsing frameworks (Tesseract, EasyOCR, LayoutLM, DocAI).
- Experience with LLM fine-tuning (LoRA, RLHF, DPO) and multimodal RAG pipelines.
- Solid grounding in classical ML and statistical methods.
- Hands-on with SQL and big data tools (Spark, Hive).
- Experience with experiment tracking (MLflow, W&B;) and deployment (Docker, REST APIs, cloud ML services).

Preferred Qualifications

- Experience in safety-critical domains (aerospace, manufacturing, healthcare).
- Contributions to open-source ML/CV/Document AI projects.
- Familiarity with MLOps practices: CI/CD for ML, model monitoring, drift detection.
- Cloud platform experience — AWS SageMaker, GCP Vertex AI, or Azure ML.
- Knowledge of edge deployment (TensorRT, ONNX, quantisation/pruning).

📌 Data Scientist (Telangana)
🏢 AIonOS
📍 Telangana