AI/ML Developer

Job description

**Key Skills:** Machine Learning, AWS, AI/ML, Fast Api, Generative AI, Python, Tensorflow, Agentic AI, Open AI **Roles and Responsibilities:** - Design, develop, and deploy AI/ML and Generative AI solutions for production environments. - Build and maintain scalable machine learning pipelines and MLOps workflows. - Develop REST APIs using FastAPI to serve AI/ML models and applications. - Train, evaluate, optimize, and deploy machine learning models using TensorFlow, PyTorch, or Keras. - Develop and integrate LLM-based applications using OpenAI APIs and Agentic AI frameworks. - Implement CI/CD pipelines for ML model deployment and automate model lifecycle management. - Deploy AI solutions on cloud platforms such as AWS, Azure, or GCP using containerized environments. - Collaborate with data engineering and cross-functional teams to integrate AI solutions into enterprise applications. - Monitor model performance, troubleshoot production issues, and continuously improve AI systems. - Document technical designs, deployment processes, and best practices. **Skills Required:** - Strong experience in Machine Learning, AI/ML, and Generative AI. - Proficiency in Python and ML frameworks such as TensorFlow, PyTorch, or Keras. - Experience developing APIs using FastAPI. - Hands-on experience with OpenAI APIs, LLMs, Prompt Engineering, and Agentic AI. - Experience building and deploying production-grade ML pipelines. - Strong understanding of MLOps, ML lifecycle, model deployment, and monitoring. - Experience with MLflow, Kubeflow, AWS SageMaker, Vertex AI, Airflow, Prefect, DVC, or Weights & Biases. - Knowledge of Docker, Kubernetes, Kafka, Git, and CI/CD tools such as Jenkins. - Experience working with AWS, Azure, or GCP cloud platforms. - Understanding of statistical modeling, data mining, and unstructured data analytics. - Experience integrating AI solutions with data engineering pipelines. Good to Have: - Experience with cloud orchestration and infrastructure automation. - Knowledge of Retrieval-Augmented Generation (RAG), vector databases, and AI agents. - Familiarity with third-party API integrations and microservices architecture. - AWS, Azure, or Google Cloud AI/ML certifications. **Education:** Bachelor's Degree in related field