- Design and implement full-stack scalable AI/ML systems integrating frontend (React/Angular), backend (Python/Node.js), RESTful APIs, RAG and agentic frameworks (LangChain, AutoGen).

- Architect distributed, microservice-based AI pipelines using event-driven architectures and containerized deployments on Azure and AWS (secondary).

- Advanced ML/DL models including GPT, BERT, CNNs, RNNs, GANs for NLP, computer vision, and multimodal applications.

- Engineer prompt-based systems using retrieval-augmented generation (RAG), few-shot learning, and prompt tuning to optimize LLM performance.

- Lead data preprocessing workflows with Spark, Pandas, and MLFlow, including feature engineering and data augmentation.

- Model observability frameworks for drift detection, performance monitoring, and lifecycle management using tools like Prometheus and Grafana.

- Apply model optimization techniques such as quantization, pruning for real-time, low-latency inference.

- Enforce AI/ML system security by integrating data protection protocols, implementing role-based access control (RBAC), encryption standards (TLS, AES), and ensuring compliance with regulatory frameworks such as GDPR, HIPAA, and ISO/IEC 27001 through automated policy enforcement and audit logging.

- Ensure AI security, governance through bias mitigation, explainability and secure deployment aligned with compliance standards.

- Conduct technical feasibility assessments, PoCs, and infrastructure cost estimation with risk analysis.

- Lead AI architecture with cross-functional collaboration, Agile execution, and continuous innovation while maintaining technical excellence and comprehensive documentation.

- Exceptional ability to articulate complex AI and technical concepts with clarity, enabling effective communication across technical and non-technical stakeholders.

Qualifications :

- Bachelor's or Masters degree in Computer Science, Data Science, or related field with 10+ years in AI/ML architecture and enterprise-grade solution delivery.

- Over 5 years of specialized experience in architecting and deploying AI/ML systems, with a demonstrated history of delivering production-grade solutions across diverse use cases.

- Hands-on experience with agentic frameworks (LangChain, AutoGen), prompt engineering, and scalable LLM deployment.

- Strong expertise in GenAI, Machine Learning, Data Science, transformer models (GPT, BERT), deep learning (CNNs, RNNs, GANs), and multimodal AI systems.

- Proficient in Python and ML/DL frameworks like TensorFlow, PyTorch, Hugging Face, and MLFlow.

- Skilled in designing distributed AI pipelines using microservices, containerization (Docker, Kubernetes) and cloud platforms (Azure, AWS-secondary).

- Deep understanding of data engineering, CI/CD automation, model observability, and performance optimization.

- Specialized in AI security including adversarial robustness, federated learning, encryption standards, and regulatory compliance.

- Proven leadership in Agile environments, technical mentoring, and translating business needs into scalable AI architectures.

- Passionate about innovation with a track record of integrating emerging AI technologies into production-ready systems.

Preferred Skills :

- Proven experience in developing AI-powered solutions across industries.

- Strong understanding of MLOps practices, CI/CD pipelines, and Azure DevOps for scalable AI deployment.

- Solution Architect Certifications is plus advantage like AZ-305: Azure Solutions Architect Expert, AWS Certified Solutions Architect.

- Deep knowledge of secure and ethical AI design, including data privacy regulations and responsible AI principles.

- Demonstrated success in leading GenAI/ML projects from concept to production with measurable impact.

- Advanced problem-solving and analytical skills for translating complex challenges into efficient AI solutions.

- Excellent communication and collaboration abilities to bridge technical and non-technical stakeholders.