AI / ML engineer

Job ID: SRJ_7685Posted: 2025-10-07Location: Sunnyvale, CASalary: USD 128-130 / Yearly (Full Time)Employment Type: Full TimeIndustry: Computer SoftwareClient: WiproContact: Meghana GorusuCompany: SRI Tech SolutionsJob Title: AI / ML engineerLocation: Sunnyvale, CANOTE: Candiate will be sent a code signal test from UBER. They need to complete the test within 48 hrs they receive it. Please inform they in the initial conversation itself. If they are not willing to, please do not submit.We are seeking a highly skilled and experienced Senior Machine Learning Engineer / AI Solutions Architect with over 7 years of hands-on experience in designing, developing, and deploying machine learning models and AI solutions. The ideal candidate will have a strong background in data science, deep learning, and cloud-based model deployment, with a proven ability to solve complex business problems and deliver scalable, production-ready AI systems.Key Responsibilities:Design and implement end-to-end machine learning pipelines including data preprocessing, feature engineering, model training, evaluation, and deployment.Develop and optimize ML/DL models for NLP, computer vision, and predictive analytics.Integrate AI models into production systems using APIs and cloud-native services.Collaborate with cross-functional teams to understand business requirements and translate them into technical solutions.Conduct exploratory data analysis and derive actionable insights from complex datasets.Apply transfer learning and advanced techniques for image classification, object detection (YOLOv8), and generative AI (Stable Diffusion, SAM).Monitor model performance, retrain models, and manage versioning using MLOps best practices.Build interactive dashboards and visualizations using Streamlit, Matplotlib, and Seaborn.Work with cloud platforms (AWS, Azure, GCP) for scalable model deployment and data pipeline orchestration.Technical Skills:Programming Languages: Python, SQL, OOPsMachine Learning & AI: ML, DL, NLP, Computer Vision, Feature Engineering, Model Evaluation, Transfer LearningFrameworks & Libraries: scikit-learn, TensorFlow, Keras, PySpark, NumPy, pandasVisualization Tools: Matplotlib, Seaborn, Advanced ExcelCloud Platforms:AWS: S3, Lambda, EC2, SageMaker, Glue, Athena, API Gateway, OpenSearch, BedrockAzure: Azure ML Studio, Azure Functions, Azure SQLGCP: Vertex AI, BigQueryDatabases: MySQL, PostgreSQL, BigQueryModel Deployment & APIs: FastAPI, Flask, StreamlitGenerative AI & CV Tools: YOLOv8, SAM, Stable Diffusion (2.1, 2.x, tiny-sd)Preferred Qualifications:Bachelor’s or Master’s degree in Computer Science, Data Science, AI, or related field.Strong understanding of MLOps, CI/CD pipelines, and containerization (Docker).Experience with version control systems like GitHub.Excellent problem-solving and communication skills.Ability to work independently and mentor junior team members.What We Offer:Opportunity to work on cutting-edge AI technologies and impactful projects.Collaborative and innovative work culture.Screening Questions:Can you briefly walk me through your professional background and experience in AI/ML?I have over 7 years of experience in designing, developing, and deploying machine learning and AI solutions. My work has spanned across predictive modeling, NLP, computer vision, and deep learning. I’ve led end-to-end ML pipelines—from data preprocessing and feature engineering to model deployment using AWS SageMaker and Azure ML Studio.I specialize in building scalable AI systems that solve real-world problems. I’ve worked on projects involving image classification using YOLOv8, NLP pipelines for text summarization, and predictive analytics for business intelligence. I’m also experienced in integrating these models into production using APIs and cloud services like AWS Lambda and Azure Functions.What kind of projects have you worked on that had a direct business impact?One of my key projects involved developing a predictive maintenance model for a manufacturing client, which reduced downtime by 30%. I used time-series forecasting and anomaly detection techniques and deployed the model using AWS SageMaker.I built a computer vision solution for automated quality inspection using YOLOv8 and OpenCV, which improved defect detection accuracy by 40% and significantly reduced manual effort.Are you comfortable working in a cloud-based environment? Which platforms have you used?Yes, I’ve extensively worked with AWS, Azure, and GCP. On AWS, I’ve used services like S3, Lambda, EC2, SageMaker, and Glue. I’ve also deployed models using Azure ML Studio and GCP Vertex AI.How do you ensure your models are production-ready and scalable?I follow a structured MLOps approach—starting with robust data validation, followed by model versioning, containerization using Docker, and CI/CD pipelines for deployment. I also monitor model drift and performance post-deployment.I use tools like MLflow for tracking experiments and SageMaker for scalable deployment. I also build REST APIs using FastAPI or Flask to serve models and integrate them with front-end applications or business systems.SRI Tech Solutions is an equal opportunity employer and does not discriminate on the basis of race, color, gender, religion, age, sexual orientation, national origin or citizenship status or ethnic origin, disability, marital status, veteran status, or any other occupationally irrelevant criteria.