Job Summary

Design and Develop AI/ML solutions using enterprise approved tools/technologies.Develop reusable AI capabilities (“skills”) such as copilots, agents, APIs, and domain-specific automation components for enterprise use.Build and operationalize AI-powered skills including coding assistants, recommendation engines, search assistants, and workflow automation tools.Develop and implement efficient Retrieval-Augmented Generation (RAG) model-based generative AI solutions, including text-to-code, diagram-to-code, text-to-recommendations, and diagram-to-recommendations services.Implement AI-based searches, chatbots, and APIs to enhance user experience and functionality.Design and develop generative AI, Open API, Large Language Models (LLM), RAG, or AI agent-based implementations.Utilize vector databases and indexing techniques to optimize AI solutions.Work in cloud environments such as Azure (preferred) or AWS (acceptable) to deploy and manage AI solutions.Experience designing or implementing agent-based AI systems, including:Tool invocation and function callingMulti-step decision and reasoning chainsAutonomous task execution and completionFamiliarity with agent orchestration frameworks or patterns like LangGraph, AutoGen or Crew AI.

Key Responsibilities

1. Develop and implement time series forecasting models using Python, scikit-learn, TensorFlow, and StatsModels, optimizing for accuracy and scalability.
2. Integrate and process large-scale streaming data with Apache Kafka and Spark, enabling real-time model training and inference.
3. Evaluate machine learning models using cross-validation, ROC/AUC, Precision/Recall, F1-score, and confusion matrix to ensure robust predictive performance.
4. Apply advanced NLP techniques with NLTK and SpaCy to extract and preprocess relevant features for forecasting tasks.
5. Optimize model deployment pipelines using Apache Airflow and Hadoop, ensuring efficient workflow orchestration and data management.
6. Collaborate within the development team to troubleshoot, refine, and enhance ML solutions, ensuring adherence to best practices and coding standards.

Skill Requirements

1. Advanced Proficiency In Machine Learning And Natural Language Processing, With Solid Experience In Time Series Analysis And Forecasting.
2. Strong Skills In Python Programming, Including Numpy, Pandas, Scikitlearn, Tensorflow, Pytorch, Xgboost, And Lightgbm.
3. Indepth Knowledge Of Distributed Data Processing With Apache Spark And Realtime Data Integration Using Apache Kafka.
4. Solid Understanding Of Ml Model Evaluation Metrics And Techniques, Including Crossvalidation And Performance Optimization.
5. Experience With Workflow Orchestration Tools Such As Apache Airflow And Big Data Platforms Like Hadoop.
6. Advanced Proficiency In Nlp Libraries Including Nltk And Spacy.

Other Requirements

1. Optional But Valuable:
2. Certifications Such As Tensorflow Developer Certificate
3. - Aws Certified Machine Learning � Specialt