AI Technology Leadership Role

Key Role: Artificial Intelligence Engineering ManagerJob DescriptionThis is an exciting opportunity to work with a globally recognized leader in the automotive industry.You will be part of a diverse team working alongside leading technical experts from around the world, on various projects involving AI solutions to solve supply chain problems.The role entails:Designing NLP/LLM applications using robust coding practicesDeveloping Agentic AI systems leveraging MCP protocol (or Google ADK)Exploring state-of-the-art models and techniques for automotive industry use casesConducting ML experiments to train and infer models, building models within memory and latency restrictionsDeploying REST APIs or minimalistic UIs for NLP applications using Docker and Kubernetes toolsShowcasing NLP/LLM applications to users through web frameworks like Dash, Plotly, Streamlit, etc.Merging multibots into super apps using LLMs with multimodalitiesCreating agentic workflows using Autogen, Agentbuilder, langgraph, and MCP protocolsDeveloping modular AI/ML products for consumption at scaleThe ideal candidate should have experience in:LLM models like Gemini, GPT5, and moreWorking through the complete lifecycle of Gen AI model development, from training and testing to deployment and performance monitoringBuilding and deploying Agentic AI products leveraging MCP protocol (or Google ADK)Developing and maintaining AI pipelines with multimodalities like text, image, audio, etc.Implementing real-world Chatbots or conversational agents at scale handling different data sourcesExperience in developing Image generation/translation tools using latent diffusion models like Stable Diffusion, Instruct Pix2PixFamiliarity with handling large-scale structured and unstructured dataEfficiently handling large-scale generative AI datasets and outputsFamiliarity with Docker tools, pipenv/conda/poetry environmentsComfort level following Python project management best practices (setup.py, logging, pytests, relative module imports, Sphinx docs, etc.)Familiarity with using GitHub (clone, fetch, pull/push, raising issues and PR, etc.)High familiarity with DL theory and practices in NLP applicationsComfort level coding in Huggingface, LangChain, Chainlit, TensorFlow, and/or PyTorch, Scikit-learn, Numpy, PandasComfort level using two or more open-source NLP modules like SpaCy, TorchText, Fastai.text, Farm-Haystack, and othersKnowledge in fundamental text data processing (regex, token/word analysis, spelling correction/noise reduction in text, segmenting noisy unfamiliar sentences/phrases at right places, deriving insights from clustering, etc.)Implemented BERT or other transformer fine-tuned models (Seq classification, NER or QA) from data preparation, model creation, and inference till deploymentUse of GCP services like BigQuery, Cloud Function, Cloud Run, Cloud Build, Vertex AIGood working knowledge on other open-source packages to benchmark and derive summaryExperience in using GPU/CPU of cloud and on-prem infrastructuresSkillset to leverage cloud platform for Data Engineering, Big Data, and ML needsUse of Dockers (experience in experimental docker features, docker-compose, etc.)Familiarity with orchestration tools like Airflow, KubeflowExperience 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 toolsGood 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 plusRequirementsWe are looking for a skilled individual with:Languages: Python, C++, JavaFrameworks: Huggingface, LangChain, Chainlit, TensorFlow, PyTorchPlatforms: Linux, Windows, macOSBenefitsWe offer competitive compensation and benefits package including health insurance, retirement plan, and paid time off.