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AI/ML Model Development
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Design, develop, and optimize machine learning and deep learning models
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Build NLP, computer vision, or predictive analytics solutions
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Train, test, and evaluate models for accuracy, scalability, and performance
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Fine-tune pre-trained models (e.g., LLMs, transformers) for business use cases
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Data Engineering & Processing (Optional)
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Collect, clean, and preprocess structured and unstructured datasets
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Work with large-scale data pipelines and streaming data systems
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Implement feature engineering and data transformation workflows
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Deployment & MLOps
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Deploy models into production using APIs, containers, or microservices
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Work with DevOps to build CI/CD pipelines for ML workflows (MLOps)
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Monitor model performance, drift, and reliability in production
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Optimize latency, throughput, and cost efficiency
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Cloud & System Integration
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Integrate AI solutions into cloud platforms (AWS)
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Work with services like SageMaker, Azure ML, Vertex AI, or OpenAI APIs
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Collaborate with DevOps, backend, and frontend teams for implementation
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Research & Innovation
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Stay up to date with emerging AI technologies and frameworks
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Evaluate and implement GenAI, LLMs, and prompt engineering techniques
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Prototype and experiment with new AI-driven solutions
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Programming: Python (preferred), Java, or Scala
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ML Frameworks: TensorFlow, PyTorch, Scikit-learn
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AI/GenAI: prompt engineering (preferred), Claude CLI / Code(preferred), LLMs(preferred) Hugging Face, OpenAI APIs
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Data Tools (Optional): Pandas, NumPy, Spark
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APIs & Microservices development
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Version control (Git)
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Cloud & DevOps
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Experience with AWS
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Containers: Docker, ECS/EKS
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CI/CD pipelines (GitHub Actions Or Jenkins Or GitLab CI)
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Data & Systems
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Databases: SQL, NoSQL
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Familiarity with data pipelines and ETL processes (Optional)
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Understanding of distributed systems
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