AI ML

Job Summary

We are looking for a highly skilled and visionary AI/ML professional to join our Workforce Management technology group. You will be responsible for building, deploying, and scaling advanced machine learning models that solve complex business problems. The ideal candidate will bridge the gap between Classical Machine Learning and the new frontier of Generative AI, specifically focusing on developing Agentic AI systems and Transformer-based architectures.

End-to-End ML Development: Lead the design and implementation of classical ML models (Regression, Clustering, Classification) to optimize workforce forecasting and resource allocation. Deep Learning & NLP: Architect and train Neural Networks and Transformer-based models for complex text processing and internal knowledge retrieval tasks. Generative AI & LLMs: Design and deploy solutions using Large Language Models (LLMs). Implement advanced techniques such as RAG (Retrieval-Augmented Generation) and fine-tuning. Agentic AI Systems: Build autonomous AI Agents capable of multi-step reasoning, tool-use, and complex task execution to automate business workflows. Productionalization (MLOps): Oversee the transition of models from notebook/PoC to high-scale production environments, ensuring robust monitoring and versioning. Technical Mentorship: Act as a Subject Matter Expert (SME) for the Chennai/Hyderabad teams, conducting code reviews and guiding junior associates on AI best practices.

Classical & Deep Learning. Statistical Modeling: Expert knowledge of Regression, Decision Trees, Random Forests, and SVMs. Unsupervised Learning: Mastery of Clustering (K-Means, DBSCAN) and Anomaly Detection. Neural Networks: Hands-on experience with CNNs, RNNs, and LSTMs using frameworks like PyTorch or TensorFlow.

Transformers: Deep understanding of Attention mechanisms, BERT, and GPT architectures. LLM Frameworks: Proficiency in LangChain, LlamaIndex, or Haystack. Agentic AI: Demonstrated experience building AI Agents (using frameworks like AutoGPT, CrewAI, or Microsoft Autogen). Vector Databases: Practical experience with Pinecone, Milvus, or Weaviate for semantic search.

Languages: Advanced Python (Pandas, NumPy, Scikit-learn). API Development: Building RESTful services for ML model consumption (FastAPI/Flask). Cloud Platforms: Experience deploying AI services on AWS (SageMaker), Azure (Azure AI), or GCP (Vertex AI).