Lead AIML

We are seeking a skilled and passionate ML/AI Engineer with 35 years of experience to join our AI innovation team.
The ideal candidate will have strong hands-on experience in Machine Learning, Generative AI, Natural Language Processing (NLP), and cloud platforms like AWS and Azure. You will work on building and deploying scalable AI/ML solutions that power real-world applications across industries such as healthcare, publishing, e-learning, and enterprise automation.

Key Responsibilities

• Design, develop, and deploy scalable ML and NLP models for real-world applications
• Build and optimize Generative AI solutions (LLMs, RAG pipelines, prompt engineering, fine-tuning)
• Perform data preprocessing, feature engineering, model selection, training, and evaluation
• Integrate ML models into production systems using MLOps best practices
• Use AWS (SageMaker, Lambda, EKS, Bedrock) and Azure (ML Studio, App Services, AKS) for training, hosting, and monitoring models
• Collaborate with product, engineering, and business teams to translate requirements into AI solutions
• Stay updated with the latest research and trends in ML, GenAI, and NLP
• Participate in code reviews, knowledge sharing, and technical documentation

Key Requirements

• 3–5 years of hands-on experience in Machine Learning and AI
• Strong knowledge of NLP techniques (NER, text classification, embeddings, summarization, etc.)
• Experience working with LLMs (GPT, BERT, T5, LLaMA, etc.), Transformers, and RAG pipelines
• Proficiency in Python and libraries like scikit-learn, PyTorch, TensorFlow, Transformers (Hugging Face)
• Familiarity with MLOps tools and frameworks (MLflow, SageMaker Pipelines, Azure ML Pipelines)
• Experience in deploying and monitoring models in AWS and/or Azure environments
• Good understanding of CI/CD, Docker, REST APIs, and containerized model deployment
• Strong problem-solving, communication, and collaboration skills

Preferred Qualifications

• Experience with Vector Databases (e.g., FAISS, Pinecone, Weaviate)
• Experience working on agentic workflows, LangChain, or prompt orchestration
• Exposure to data annotation tools, model evaluation, and prompt testing frameworks
• Certifications in AWS/Azure ML-related tracks are a plus
• Open-source contributions or portfolio of AI projects on GitHub