Senior AI Engineer- #26-13102

Summary

We are seeking a highly skilled Senior AI Engineer to design, build, and deploy scalable artificial intelligence and machine learning solutions. This role requires strong expertise in developing predictive models, implementing AI-powered applications, and integrating them into enterprise systems. The ideal candidate combines deep technical proficiency with practical experience in cloud platforms and production-grade AI systems.

Required Skills & Experience

• Overall minimum of 8 years of IT experience with minimum of 2 years of required AI tool working experience
• 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

Roles & Responsibilities

• 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
• 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, Clienture 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