Agentic AI Engineering Manager

Short Description:

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.

Description

Background:

Fords Supply Chain Analytics Team is looking for an AI Engineering Manager experienced in NLP/LLM/GenAI, who is 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.

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.

Qualification:

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

Education: Masters 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 Chatbots 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 , 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 or more of open source NLP modules like SpaCy, TorchText, , 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.