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Agentic AI Engineering Manager - Supply Chain

Agentic AI Engineering Manager - Supply Chain

Allegis Global SolutionsChennai, Tamil Nadu, India
9 days ago
Job description

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.
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    Supply Chain Manager • Chennai, Tamil Nadu, India

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