AI/ML Architect

Job Summary:

We are seeking a highly experienced AI Solutions Architect with deep expertise in Artificial Intelligence, Machine Learning, and Generative AI (GenAI). This role involves leading the design, development and deployment of scalable AI/ML solutions and tools with a strong focus on innovative architectures. The ideal candidate will collaborate across teams to deliver high-performance AI systems aligned with business objectives and future scalability.

In this Role, Your Responsibilities Will Be:

Design and implement full-stack scalable AI/ML systems integrating frontend (React/Angular), backend ), 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 Master's 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