[Apply Now] 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 industry Description Background: 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: 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 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.