- About Job/Role

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.
Design and implement machine learning models leveraging advanced Generative AI (GenAI) techniques for content generation, summarization, text classification, and intelligent automation.
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.
Develop Retrieval-Augmented Generation (RAG) pipelines, integrate GenAI APIs and services into internal platforms, and implement guardrails for responsible AI practices.
Collaborate with product and engineering teams to deliver GenAI-powered features, drive model governance, and mentor junior team members.
Experience with MLOps, CI/CD, feature engineering, dimensional modeling, and data engineering.

- Roles and Responsibilities

Lead the complete AI/ML pipeline from data ingestion and exploration to model deployment and monitoring.
Lead development, build, and test scenarios 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) with real-world business applications.
Leverage advanced GenAI techniques for content generation, summarization, text classification, and intelligent automation.
Fine-tune, optimize, and deploy LLMs (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 RAG pipelines for domain-specific applications using vector databases and embedding models.
Integrate GenAI APIs and services (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 for 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 MLOps and CI/CD best practices.
Work on state-of-the-art architectures including Transformers, LSTM, CNNs for NLP, computer vision, and deep learning use cases.
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.
Mentor junior data scientists and ML engineers on best practices, tools, and techniques.

- Required Qualification

Bachelor’s, Master’s, or PhD in Computer Science, Data Science, Statistics, Mathematics, or related field.

- Experience

10–12 years of experience in applied machine learning and artificial intelligence.

- Skills

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 (BERT, GPT), RNNs, CNNs, etc.
Solid experience with GenAI tools and frameworks (OpenAI, LangChain, HuggingFace Transformers).
Proficiency in Python and ML libraries (scikit-learn, TensorFlow, PyTorch, XGBoost).
Strong knowledge of model deployment using Docker, Kubernetes, MLflow, or similar tools.
Experience with large-scale datasets and distributed computing frameworks like Spark (plus).
Good understanding of data engineering practices, ETL pipelines, and dimensional modeling.
Working knowledge of SQL, Snowflake, and cloud data warehouses (advantageous).
Familiarity with MLOps principles, version control (Git), and model monitoring tools.
Experience with cloud platforms (Azure, AWS, GCP) for scalable AI/ML deployment.
Experience with CI/CD pipelines in ML model delivery.
Excellent communication and stakeholder management skills.

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