Role Summary
We are seeking a Senior AI & Generative AI Specialist to architect, build, and scale production-grade AI and GenAI solutions. The role demands deep hands-on expertise, strong system architecture skills, and the ability to lead cross-functional teams delivering result oriented & compliant AI systems.
This role will own end-to-end AI lifecycle - from problem framing and model design to deployment, monitoring, governance, and business impact - with a strong emphasis on Machine learning,GenAI, LLM fine-tuning, RAG systems, and Responsible AI.
Key Responsibilities
AI & GenAI Architecture
- Design and architect enterprise-scale AI and Generative AI systems, including LLM-based applications, RAG pipelines, fine-tuned models, and multimodal AI systems.
- Lead development of AI platforms and frameworks enabling reusable, scalable AI services (AI-as-a-Service).
- Define model selection strategies , fine-tuning approaches, and inference optimization.
Machine Learning & Deep Learning
- Develop and deploy advanced ML/DL models across:
- Computer Vision (segmentation, detection, classification)
- NLP (BERT, GPT, Transformers)
- Generative AI (Diffusion models, GANs, multimodal systems)
- Time-series forecasting, predictive analytics, anomaly detection
- Drive model optimization, hyperparameter tuning, and performance benchmarking.
- Ensure model explainability, fairness, bias detection, and mitigation.
GenAI & LLM Systems
- Build GenAI applications & Agents including:
- Intelligent document processing
- Automated report generation
- Smart ticketing and customer escalation systems
- Knowledge assistants using RAG + vector databases
- Implement prompt engineering, evaluation frameworks, and guardrails.
- Optimize inference cost, latency, and scalability in cloud environments.
MLOps & Production Deployment
- Establish MLOps best practices:
- CI/CD for ML
- Model versioning and monitoring
- Automated retraining pipelines
- Deploy AI services using Docker, Kubernetes, MLflow, FastAPI, Flask.
- Ensure high availability, low latency, and cloud cost optimization.
Cloud & Big Data
- Architect AI workloads on Azure, Databricks, Spark.
- Build scalable data pipelines for large-scale training and inference.
- Leverage distributed computing for large datasets and real-time inference.
Leadership & Stakeholder Engagement
- Consult and mentor AI engineering and data science teams.
- Collaborate with the AI working group & international stakeholder community.
- Translate business and domain problems into AI solutions with measurable impact.
- Drive innovation initiatives, patents, and hackathon-level experimentation.
