Generative AI/ML Engineer

Job Description : Key Responsibilities : - Design, develop, and deploy applications leveraging LLMs (OpenAI, Gemini, etc.) for specific business use cases. - Implement and optimize RAG pipelines to enhance LLM response accuracy and relevance. - Fine-tune pre-trained LLMs using techniques like PEFT, LoRA, and QLoRA to improve task-specific performance. - Develop and implement NLP solutions for sentiment analysis, topic modeling, information extraction, and other text-based tasks. - Utilize advanced NLP techniques (Seq2Seq, attention models, tokenizers, etc.) to extract meaningful insights from text data. - Develop rapid POCs and MVPs to demonstrate the feasibility of new AI/ML solutions across infrastructure, storage, analytics, AI/ML, and security. - Document and present findings and recommendations to stakeholders. - Design and build scalable and cost-effective AI/ML solutions on Azure cloud platforms, ensuring adherence to best practices. - Deploy and maintain AI/ML models in production environments. - Research and evaluate emerging AI/ML technologies and trends, identifying opportunities for innovation. - Translate research findings into actionable recommendations for product development. - Implement efficient tools and processes to improve development workflows and ensure high code quality. - Contribute to the development and maintenance of AI/ML best practices. - Observe and assess related technology solutions, aligning with company tech strategy and acquisition goals. - Predict technology applications for business, including long-term trends and impact on product roadmaps. - Support business development initiatives by contributing to solutioning, estimations, and RFI/RFP responses. - Provide technical expertise during sales engineering activities. Must-Have Skills : - Strong hands-on experience with Python-based web and backend frameworks (Django, Flask, FastAPI). - Extensive experience with LLMs (OpenAI, Gemini, Mistral, Falcon, etc.) - Retrieval Augmented Generation (RAG): Advanced knowledge of RAG and Multi-Modal RAG. - Proficiency with LangChain and Llama. - LLM Fine-tuning : Experience with PEFT, LoRA, QLoRA, and Adapters. - Text Analytics : Expertise in NLP, sentiment analysis, topic modeling, Seq2Seq, attention models, tokenizers, TF-IDF, Word2Vec. - Advanced proficiency in Python, PyTorch, Keras, TensorFlow, OpenCV, SpaCy, Scikit-learn, Pandas, Git, SQL, Databricks, R. - Proficiency in SQL and NoSQL Skills : - Machine Learning Techniques - Neural Networks - Deep Learning (CNN/RNN/LSTM) - Predictive modeling, EDA - Linear/Logistic Regression - Clustering, Nave Bayes - Ensemble, Gradient Boosting - XGBoost, SVM - Decision Trees - Random Forests, Knowledge Graphs. (ref:hirist.tech)

Location: pune, IN

Posted Date: 3/29/2025