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 KubernetesExperience with ML frameworks (PyTorch, TensorFlow, Hugging Face Transformers)Proficiency in Python and modern software development practicesExperience with cloud platforms (AWS, GCP, or Azure) and their AI / ML servicesKey Responsibilities :
AI Solution Design & Implementation :
Architect end-to-end AI systems leveraging large language models and generative AI technologiesDesign scalable, production-ready AI applications that meet business objectives and performance requirementsEvaluate and integrate LLM APIs from leading providers (OpenAI, Anthropic Claude, Google Gemini, etc.)Establish best practices for prompt engineering, model selection, and AI system optimizationModel Development & Fine-tuning :
Fine-tune open-source models (Llama, Mistral, etc.) for specific business use casesImplement custom training pipelines and evaluation frameworksOptimize model performance, latency, and cost for production environmentsStay current with latest model architectures and fine-tuning techniquesInfrastructure & Deployment :
Deploy and manage AI models at enterprise scale using containerization (Docker) and orchestration (Kubernetes)Build robust, scalable APIs using FastAPI and similar frameworksDesign and implement MLOps pipelines for model versioning, monitoring, and continuous deploymentEnsure high availability, security, and performance of AI systems in productionBusiness & Technical Leadership :
Collaborate with stakeholders to understand business problems and translate them into technical requirementsProvide technical guidance and mentorship to development teamsConduct feasibility assessments and technical due diligence for AI initiativesCreate technical documentation, architectural diagrams, and implementation roadmapsCore Competencies :
Strong understanding of transformer architectures, attention mechanisms, and modern NLP techniquesExperience with MLOps tools and practices (model versioning, monitoring, CI / CD)Ability to translate complex business requirements into technical solutionsStrong problem-solving skills and architectural thinking(ref : hirist.tech)