Role & responsibilities Our company is looking for an experienced Technical Consultant / Senior Technical Consultant in .Net Core, Web API, SQL Server, etc. As an API Developer, you will participate in all aspects of the software development lifecycle which includes estimating, technical design, implementation, documentation, testing, deployment, and support of applications. As a member working in a team environment, you will work with solution architects and developers on functional requirements. Find below the expected list of essential skills and qualifications. While this is our ideal list, we will consider candidates that do not necessarily have all the qualifications but have sufficient experience and talent. Essential Skills 10+ years experience in design, develop, and maintain Microsoft-based software and web applications. Expertise knowledge in C#, ASP.NET, Web API with hands on experience in .NET framework 4.0+ and .NET Core 3.0+. Hands on experience into MSSQL, SQL Server Queries, Stored Procedure, Triggers, etc. Proficient understanding of web verbs, JSON and XML protocols, style sheets, Rest API’s, Microservices, JavaScript, Swagger and Postman. Unit and functional testing experience, for example with NUnit, XUnit. Proficient in MS Visual Studio, Bitbucket, Jira, Git including application build/ deployment. Knowledge of hosting platform like Cloud based – AWS preferred. Software development lifecycle experience and experience working in an Agile methodology. Ability to proactively engage and drive the tasks. Work with Business Stakeholders, Product Owners, Business Analysts to understand functional and non-functional requirements and interact with other cross-functional teams. Conduct code reviews, employ strong coding standards for efficiency, readability, and reuse. Experience in Logistic/Retail/Healthcare domain Strong communication, interpersonal, collaboration and leadership skill Excellent problem-solving skills and ability to think logically. Adept at learning new technologies quickly.

AI in Cloud Deployment, combining AI/ML expertise with cloud infrastructure and DevOps skills Job Title: Devops Engineer Location: Chennai Job Type: Full-Time Job Summary: We are looking for a highly skilled AI Cloud Deployment Engineer to join our team to build, deploy, and scale AI/ML models and pipelines in cloud environments. The ideal candidate has strong experience with cloud platforms (Azure, AWS, GCP), containerization, CI/CD, and MLOps best practices. You will work closely with data scientists, ML engineers, and DevOps teams to operationalize AI solutions. Key Responsibilities: Design and implement cloud-based deployment pipelines for AI/ML models using Azure, AWS. Collaborate with data scientists to containerize and deploy ML models using Docker and Kubernetes. Set up CI/CD pipelines for continuous model integration and deployment (MLOps). Monitor, troubleshoot, and optimize deployed AI services for performance and reliability. Manage scalable data pipelines and APIs using cloud-native technologies. Apply security best practices to model deployment and API access control. Automate infrastructure provisioning with tools like Terraform or ARM templates. Ensure reproducibility and versioning of ML experiments using tools like MLflow, DVC, or SageMaker. Build dashboards and logging/monitoring systems for AI applications. Required Skills : Hands-on experience in AI/ML model deployment in cloud environments (Azure ML, SageMaker, Vertex AI, etc.). Proficiency in cloud computing: AWS, Azure, or GCP. Experience with Docker, Kubernetes, and container orchestration. Strong understanding of MLOps , including model versioning, drift detection, and pipeline automation. Familiarity with CI/CD tools: GitHub Actions, Jenkins, GitLab CI, or Azure DevOps. Good programming skills in Python (FastAPI for serving models),Java . Experience with infrastructure-as-code (Terraform, CloudFormation, Azure Bicep). Knowledge of cloud security, networking, and identity management. Preferred Qualifications: Bachelors or Masters degree in Computer Science, Data Science, or related field. Experience with ML tools like MLflow, Kubeflow, SageMaker Pipelines. Knowledge of data engineering frameworks (Airflow, Kafka, Spark). Prior experience working in Agile/Scrum environments. Nice to Have: Exposure to edge deployment for AI models (e.g., on IoT devices). Knowledge of LLMOps and deploying generative AI models in production. Familiarity with serverless compute services (AWS Lambda, Azure Functions).