Agentic AI Engineering Manager

Short Description:This opportunity is with a globally recognized leader in the automotive industry. 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 the industryDescriptionBackground: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 mandatoryEducation: Master's degree in computer science, Engineering, Maths or SciencePerformed 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 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 or 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.