SC AI Engineering Manager

Background :

Ford’s Supply Chain Analytics Team is looking for an AI Engineering Manager experienced in NLP/LLM/GenAI, who are hands-on and can employ many NLP/Prompt engineering techniques from traditional statistical/ML NLP to DL-based sequence models and transformers in their day-to-day work. AI Technical Manager will lead a small team as well as consult across the Supply Chain Analytics Team to support the implementation of Agentic AI solutions.

You'll be working alongside leading technical experts from all around the world, on a variety of products involving Agentic AI solutions leveraging MCP to solve Supply Chain Problems across Ford Globally.

Experience : 5+ years. Management experience is preferred but not mandatory

Education : Master’s Degree in Computer Science, Engineering, Maths or Science

Performed any modern NLP/LLM courses/open competitions is also welcomed.

Technical Requirements :

• Experience in LLM models like Gemini, GPT5 and more
• Work through the complete lifecycle of Gen AI model development, from training and testing to deployment and performance monitoring.
• Experience in building and deploying Agentic AI products leveraging MCP protocol(or Google ADK)
• Developing and maintaining AI pipelines with multimodalities like text, image, audio etc.
• Have implemented in real-world Chat bots or conversational agents at scale handling different data sources.
• Experience in developing Image generation/translation tools using any of the latent diffusion models like stable diffusion, Instruct pix2pix.
• Expertise in handling large scale structured and unstructured data.
• Efficiently handled large-scale generative AI datasets and outputs.
• Familiarity in the use of Docker tools, pipenv/conda/poetry env
• Comfort level in following Python project management best practices (use of setup.py, logging, pytests, relative module imports, sphinx docs,etc.,)
• Familiarity in use of Github (clone, fetch, pull/push, raising issues and PR, etc.,)
• High familiarity in the use of DL theory/practices in NLP applications
• Comfort level to code in Huggingface, LangChain, Chainlit, Tensorflow and/or Pytorch, Scikit-learn, Numpy and Pandas
• Comfort level to use two/more of open source NLP modules like SpaCy, TorchText, fastai.text, farm-haystack, and others
• Knowledge in fundamental text data processing (like use of regex, token/word analysis, spelling correction/noise reduction in text, segmenting noisy unfamiliar sentences/phrases at right places, deriving insights from clustering, etc.,)
• Have implemented in real-world BERT/or other transformer fine-tuned models (Seq classification, NER or QA) from data preparation, model creation and inference till deployment
• Use of GCP services like BigQuery, Cloud function, Cloud run, Cloud Build, VertexAI,
• Good working knowledge on other open source packages to benchmark and derive summary
• Experience in using GPU/CPU of cloud and on-prem infrastructures
• Skillset to leverage cloud platform for Data Engineering, Big Data and ML needs.
• Use of Dockers (experience in experimental docker features, docker-compose, etc.,)
• Familiarity with orchestration tools such as airflow, Kubeflow
• Experience in CI/CD, infrastructure as code tools like terraform etc.
• Kubernetes or any other containerization tool with experience in Helm, Argoworkflow, etc.,
• Ability to develop APIs with compliance, ethical, secure and safe AI tools.
• Good UI skills to visualize and build better applications using Gradio, Dash, Streamlit, React, Django, etc.,
Deeper understanding of javascript, css, angular, html, etc., is a plus.

Miscellaneous Skills :

Data Engineering:

• Skillsets to perform distributed computing (specifically parallelism and scalability in Data Processing, Modeling and Inferencing through Spark, Dask, RapidsAI or RapidscuDF)
• Ability to build python-based APIs (e.g.: use of FastAPIs/ Flask/ Django for APIs)
• Experience in Elastic Search and Apache Solr is a plus, vector databases.

Responsibilities :

• Design NLP/LLM/GenAI applications/products by following robust coding practices
• Design Agentic AI Systems leveraging MCP protocol(or Google ADK)
• Explore SoTA models/techniques so that they can be applied for automotive industry use cases
• Conduct ML experiments to train/infer models; if need be, build models that abide by memory & latency restrictions,
• Deploy REST APIs or a minimalistic UI for NLP applications using Docker and Kubernetes tools
• Showcase NLP/LLM/GenAI applications in the best way possible to users through web frameworks (Dash, Plotly, Streamlit, etc.,)
• Converge multibots into super apps using LLMs with multimodalities
• Develop agentic workflow using Autogen, Agentbuilder, langgraph and MCP protocols
• Build modular AI/ML products that could be consumed at scale.