AI Engineer

• AI/ML Model Development
• Design, develop, and optimize machine learning and deep learning models
• Build NLP, computer vision, or predictive analytics solutions
• Train, test, and evaluate models for accuracy, scalability, and performance
• Fine-tune pre-trained models (e.g., LLMs, transformers) for business use cases
• Data Engineering & Processing (Optional)
• Collect, clean, and preprocess structured and unstructured datasets
• Work with large-scale data pipelines and streaming data systems
• Implement feature engineering and data transformation workflows
• Deployment & MLOps
• Deploy models into production using APIs, containers, or microservices
• Work with DevOps to build CI/CD pipelines for ML workflows (MLOps)
• Monitor model performance, drift, and reliability in production
• Optimize latency, throughput, and cost efficiency
• Cloud & System Integration
• Integrate AI solutions into cloud platforms (AWS)
• Work with services like SageMaker, Azure ML, Vertex AI, or OpenAI APIs
• Collaborate with DevOps, backend, and frontend teams for implementation
• Research & Innovation
• Stay up to date with emerging AI technologies and frameworks
• Evaluate and implement GenAI, LLMs, and prompt engineering techniques
• Prototype and experiment with new AI-driven solutions

Skills Must have

• Programming: Python (preferred), Java, or Scala
• ML Frameworks: TensorFlow, PyTorch, Scikit-learn
• AI/GenAI: prompt engineering (preferred), Claude CLI / Code(preferred), LLMs(preferred) Hugging Face, OpenAI APIs
• Data Tools (Optional): Pandas, NumPy, Spark
• APIs & Microservices development
• Version control (Git)
• Cloud & DevOps
• Experience with AWS
• Containers: Docker, ECS/EKS
• CI/CD pipelines (GitHub Actions Or Jenkins Or GitLab CI)
• Data & Systems
• Databases: SQL, NoSQL
• Familiarity with data pipelines and ETL processes (Optional)
• Understanding of distributed systems