Sr. Gen AI/ML Engineer (Onsite)

Cognizant•Phoenix, AZ

4h•$75,000 - $100,000•Onsite

About The Position

As an AI/ML & Generative AI Developer, you will be responsible for building and implementing machine learning and generative AI solutions that address business problem. You’ll work on developing models and integrating AI capabilities into scalable cloud-native applications using Azure. This role requires strong programming skills, practical experience with ML/GenAI frameworks, and a passion for innovation.

Requirements

Proficient in applying statistical techniques such as descriptive statistics, probability distributions, hypothesis testing, correlation analysis, ANOVA, and linear/logistic regression for data analysis and model validation.
Strong proficiency in Python and ML/GenAI libraries (e.g., scikit-learn, TensorFlow, PyTorch, Transformers, LangChain).
Hands-on experience with Azure ML and Azure OpenAI.
Practical understanding of how large language models (LLMs) work, including their architecture and components.
Experience working with embeddings for semantic search, similarity matching, and feature representation.
Familiarity with GenAI platforms (Hugging Face, LLaMA), and vector databases (FAISS, Pinecone, Azure AI Search).

Responsibilities

Create predictive models using supervised, unsupervised, and semi-supervised learning techniques.
Apply algorithms like regression, classification, clustering, and ensemble methods.
Perform exploratory data analysis (EDA), Data preprocessing, statistical testing, and feature engineering to improve model performance.
Evaluate models using metrics such as ROC-AUC, RMSE, and F1-score.
Conduct hyperparameter tuning and cross-validation.
Deploy models as APIs or services using Azure Kubernetes Service (AKS), Azure Functions, or App Services.
Integrate models into production systems with CI/CD pipelines and monitor performance.
Build GenAI applications using large language models (LLMs) like GPT, LLaMA, or Claude for tasks such as summarization, content generation, and conversational AI.
Integrate LLMs into enterprise systems using Azure OpenAI, Hugging Face, or other APIs.
Design and optimize prompts using techniques like few-shot learning, prompt chaining, and Retrieval-Augmented Generation (RAG).
Deploy GenAI models in containerized environments using AKS or Azure Functions.
Collaborate with cross-functional teams including data engineers, analysts to deliver AI/ML solutions aligned with business goals.
Use tools like JIRA to manage tasks, track progress, and ensure timely delivery of project milestones.
Participate in agile ceremonies such as sprint planning, stand-ups, and retrospectives to maintain alignment and transparency.
Communicate technical updates and insights effectively to both technical and non-technical stakeholders.