Description :

- 5+ years of experience in machine learning engineering or data science

- 1+ years of hands-on experience building, deploying, and managing generative AI models in production

- Proven track record of delivering large-scale ML solutions

Technical Skills :

- Expert-level proficiency with LLM APIs (OpenAI, Claude, Gemini, etc.)

- Hands-on experience fine-tuning transformer models (Llama, Mistral, etc.)

- Strong proficiency in FastAPI, Docker, and Kubernetes

- Experience with ML frameworks (PyTorch, TensorFlow, Hugging Face Transformers)

- Proficiency in Python and modern software development practices

- Experience with cloud platforms (AWS, GCP, or Azure) and their AI/ML services

Key Responsibilities :

AI Solution Design & Implementation :

- Architect end-to-end AI systems leveraging large language models and generative AI technologies

- Design scalable, production-ready AI applications that meet business objectives and performance requirements

- Evaluate and integrate LLM APIs from leading providers (OpenAI, Anthropic Claude, Google Gemini, etc.)

- Establish best practices for prompt engineering, model selection, and AI system optimization

Model Development & Fine-tuning :

- Fine-tune open-source models (Llama, Mistral, etc.) for specific business use cases

- Implement custom training pipelines and evaluation frameworks

- Optimize model performance, latency, and cost for production environments

- Stay current with latest model architectures and fine-tuning techniques

Infrastructure & Deployment :

- Deploy and manage AI models at enterprise scale using containerization (Docker) and orchestration (Kubernetes)

- Build robust, scalable APIs using FastAPI and similar frameworks

- Design and implement MLOps pipelines for model versioning, monitoring, and continuous deployment

- Ensure high availability, security, and performance of AI systems in production

Business & Technical Leadership :

- Collaborate with stakeholders to understand business problems and translate them into technical requirements

- Provide technical guidance and mentorship to development teams

- Conduct feasibility assessments and technical due diligence for AI initiatives

- Create technical documentation, architectural diagrams, and implementation roadmaps

Core Competencies :

- Strong understanding of transformer architectures, attention mechanisms, and modern NLP techniques

- Experience with MLOps tools and practices (model versioning, monitoring, CI/CD)

- Ability to translate complex business requirements into technical solutions

- Strong problem-solving skills and architectural thinking