Manhattan WMOS Engineer
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Ford Motor Company संयुक्त राज्य अमेरिका की एक ऑटोमोबाइल निर्माता कंपनी है, जिसका इतिहास 1903 से जुड़ा है। वाहनों के विकास और उत्पादन के अलावा, कंपनी मोबिलिटी सेवाओं, डिजिटल अनुभवों और सॉफ़्टवेयर पर भी अपना ध्यान बढ़ा रही है। Ford के कार्यों में स्थिरता से जुड़े प्रयास और पर्यावरणीय लक्ष्य भी शामिल हैं, जो परिवहन, नवाचार, सामुदायिक प्रभाव और जिम्मेदार व्यावसायिक प्रक्रियाओं को जोड़ने वाले दृष्टिकोण को दर्शाते हैं।
Data Scientist, NLP and Generative AI at Ford, India Hybrid
Develop NLP, LLM, and generative AI products for automotive use cases at Ford. Build multimodal pipelines, agentic workflows, APIs, and user-facing applications at scale.
विवरण
- Create NLP, LLM, and generative AI applications and products with disciplined coding practices.
- Evaluate advanced models and methods for automotive applications.
- Run machine learning experiments for model training and inference under memory and latency constraints.
- Deploy NLP REST APIs and lightweight user interfaces with Docker and Kubernetes.
- Present NLP, LLM, and generative AI applications using Dash, Plotly, and Streamlit.
- Combine multiple bots into multimodal, LLM-powered applications.
- Create agentic workflows with Autogen, AgentBuilder, and LangGraph.
- Develop modular AI and machine learning products designed for scalable consumption.
- Collaborate with global technical experts on classification, question answering, chatbots, translation, semantic search, and summarization products.
आवश्यकताएँ
- Bachelor’s or master’s degree in computer science, engineering, mathematics, or a related science field.
- Understanding of deep learning theory and practices for NLP applications.
- Programming experience with ADK, A2A, AgentSkills, Ontology, Hugging Face, LangGraph, LangChain, Chainlit, TensorFlow or PyTorch, scikit-learn, NumPy, and Pandas.
- Familiarity with at least two open-source NLP libraries, such as spaCy, TorchText, fastai.text, or FARM Haystack.
- Knowledge of text processing techniques including regular expressions, token and word analysis, spelling correction, noise reduction, sentence segmentation, and clustering.
- Hands-on deployment experience with BERT or other fine-tuned transformer models for sequence classification, named entity recognition, or question answering.
- Familiarity with Docker and pipenv, conda, or Poetry environments.
- Knowledge of Python project management practices including setup.py, logging, pytest, relative imports, and Sphinx documentation.
- Familiarity with GitHub workflows.
- Experience with GCP services including BigQuery, Cloud Functions, Cloud Run, Cloud Build, and Vertex AI.
- Working knowledge of open-source tools for benchmarking and summary generation.
- Experience using GPU and CPU resources in cloud and on-premises environments.
- Ability to use cloud platforms for data engineering, big data, and machine learning workloads.
- Experience with Docker, docker-compose, and experimental Docker capabilities.
- Familiarity with Airflow and Kubeflow.
- Experience with CI/CD and infrastructure-as-code tools such as Terraform.
- Experience with Kubernetes or other container platforms, including Helm and Argo Workflows.
- Ability to create compliant, ethical, secure, and safe AI APIs.
- Experience developing user interfaces or visualizations with Gradio, Dash, Streamlit, React, or Django.
- Distributed computing experience with Spark, Dask, RAPIDS AI, or cuDF.
- Ability to build Python APIs with FastAPI, Flask, or Django.
- Experience with Elasticsearch and Apache Solr is advantageous; experience with vector databases is also relevant.
- Experience working with LLMs such as GPT-5, Gemini, Kimi, or Seedance.
- Experience across the full generative AI model lifecycle, including training, testing, deployment, and performance monitoring.
- Experience developing and maintaining multimodal AI pipelines for text, image, and audio.
- Practical experience deploying chatbots or conversational agents at scale across varied data sources.
- Experience creating image generation or translation tools with latent diffusion models such as Stable Diffusion or Instruct Pix2Pix.
- Expertise working with large-scale structured and unstructured data.
- Experience managing large-scale generative AI datasets and outputs.
- Strong communication and collaboration skills across geographic regions.
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