**Job Summary** We are seeking a highly experienced AI/ML Engineer with 10–12 years of hands-on experience to lead the complete AI/ML pipeline—from data ingestion and exploration to model deployment and monitoring. The ideal candidate will design and implement machine learning models, leveraging advanced Generative AI (GenAI) techniques to drive content generation, summarization, text classification, and intelligent automation. This role requires expertise in large language models (LLMs), including fine-tuning, optimization, and deployment of models such as GPT, BERT, and LLaMA using transfer learning and LoRA techniques. The candidate will also develop Retrieval-Augmented Generation (RAG) pipelines, integrate GenAI APIs and services into internal platforms, and implement guardrails for responsible AI practices. Beyond model development, the AI/ML Engineer will collaborate with product and engineering teams to deliver GenAI-powered features, drive model governance, and mentor junior team members to foster AI innovation. Experience with MLOps, CI/CD, feature engineering, dimensional modeling, and data engineering will be essential for success in this role. This position is perfect for someone passionate about cutting-edge AI/ML advancements, Generative AI applications, and building impactful machine learning solutions for real-world business challenges. **Job Responsibility** + Lead the complete **AIML pipeline** from data ingestion and exploration to model deployment and monitoring. + Leads development, build, and test scenrios for data solutions to deliver business value while meeting quality & technical requirements + Accountable for end-to-end delivery of source data acquisition, complex transformation and orchestration pipelines, AI/ML engineering, and front-end visualization + Design, build, and **deploy machine learning models** (classification, regression, deep learning etc) with real-world business applications. + Leverage advanced **GenAI techniques** to solve problems in content generation, summarization, text classification, and intelligent automation. + Fine-tune, optimize, and deploy **LLMs** (e.g., GPT, BERT, LLaMA) for custom use cases using techniques like transfer learning and LoRA. + Develop **prompt engineering frameworks** and reusable GenAI patterns to accelerate model development. + Build **Retrieval-Augmented Generation (RAG)** pipelines for domain-specific applications using vector databases and embedding models. + Integrate **GenAI APIs and services** (e.g., OpenAI, Azure OpenAI, Hugging Face, LangChain) into internal platforms and products. + Implement **guardrails, safety filters** , and evaluation metrics for GenAI models to ensure responsible AI practices. + Perform advanced **text mining** , **semantic search** , **document clustering** , and **knowledge graph generation** using GenAI. + Drive **GenAI model governance** including versioning, bias/fairness assessments, and monitoring drift in deployed systems. + Collaborate with product and engineering teams to deliver GenAI-powered features in production systems. + Mentor junior AI/ML team members and foster adoption of GenAI techniques across the organization. + Design and implement **robust ML models** (classification, regression, deep learning) to solve real-world problems. + Apply **advanced statistical techniques** to derive insights and build predictive models. + Optimize **model performance through hyperparameter** tuning and evaluation metrics analysis. + **Deploy models to production environments** using best practices in MLOps and CI/CD pipelines. + Work on state-of-the-art architectures including Transformers, LSTM, CNNs for deep learning use cases (e.g., NLP, computer vision). + Translate complex technical concepts into clear business insights and recommendations. + Lead **end-to-end ML lifecycle** : data exploration, feature engineering, model selection, training, evaluation, deployment, and monitoring. + **Develop and fine-tune GenAI models** including foundational models and **LLMs** (fine-tuning, prompt engineering, RAG). + Mentor junior data scientists and ML engineers on best practices, tools, and techniques. **Qualifications:** Bachelore /Master’s / PhD in Computer Science, Data Science, Statistics, Mathematics, or related field. **Skills:** + 10–12 years of experience in applied machine learning and artificial intelligence. + Strong grounding in **statistics** , **probability** , and **mathematics** . + Proven experience in **feature engineering** , **model tuning** , and understanding of **data structures and algorithms** . + Deep knowledge of **classification/regression techniques** , **ensemble methods** , and **deep learning architectures** . + Expertise with **transformer models** (e.g., BERT, GPT), **RNNs** , **CNNs** , etc. + Solid experience with **GenAI** tools and frameworks (e.g., OpenAI, LangChain, HuggingFace Transformers). + Proficiency in programming languages such as **Python** , with experience using ML libraries (e.g., scikit-learn, TensorFlow, PyTorch, XGBoost). + Strong knowledge of **model deployment techniques** using Docker, Kubernetes, MLflow, or similar tools. + Experience working with **large-scale datasets** and distributed computing frameworks like **Spark** is a plus. + Good understanding of **data engineering** practices, **ETL pipelines** , and **dimensional modeling** . + Working knowledge of **SQL** , **Snowflake** , and other cloud data warehouses is an added advantage. + Familiarity with **MLOps** principles, version control (Git), and model monitoring tools. + Experience with **cloud platforms** (e.g., Azure, AWS, GCP) for scalable AI/ML deployment. + Experience with **CI/CD pipelines** in ML model delivery. + Excellent communication and stakeholder management skills.