Job Overview:

We are seeking a skilled AI/ML Architect with strong expertise in AWS and Generative AI (GenAI), along with a solid foundation in Data Engineering and Machine Learning. The ideal candidate will play a key role in shaping end-to-end AI/ML and GenAI solutions—from data architecture to model deployment—ensuring designs are scalable, secure, cost-efficient, and aligned with business outcomes.

Key Responsibilities:

Define and lead end-to-end AI/ML and Generative AI solution architectures, covering data, model, application, and integration layers.
Create architecture diagrams and design frameworks addressing scalability, cost optimization, performance, security, and enterprise adoption.
Collaborate with business and technical stakeholders to identify use cases and translate them into production-ready AI/ML and GenAI solutions.
Influence and align client VPs, Directors, and senior stakeholders on architecture decisions, roadmaps, and solution approaches.
Architect and implement data pipelines and feature engineering frameworks for structured and unstructured data.
Design and oversee the full ML lifecycle, including data preparation, model development, evaluation, deployment, and monitoring.
Drive adoption of MLOps and LLMOps practices, including CI/CD pipelines, model governance, prompt/version management, and retraining strategies.
Leverage AWS cloud services (SageMaker, Bedrock, Lambda, S3, EMR, etc.) to build scalable AI platforms.
Apply Design Thinking principles to ensure user-centric and business-aligned solutions.
Mentor engineering and data science teams on architecture patterns, performance optimization, and system integration.

Required Qualifications:

Bachelor's or master's degree in computer science, Data Science, Engineering, or a related field.
12+ years of experience in software/data engineering, with at least 4+ years in AI/ML solution architecture.
Strong expertise in data engineering (ETL pipelines, data lakes/warehouses, real-time data processing).
Hands-on experience with ML frameworks (TensorFlow, PyTorch, Scikit-learn) and platforms (SageMaker, Azure ML, GCP AI).
Deep understanding of MLOps principles and tools (MLflow, Kubeflow, Airflow, Docker, Kubernetes).
Proficient in cloud platforms: AWS, Azure, or GCP.
Strong problem-solving and communication skills, with a passion for applying Design Thinking to deliver impactful solutions.
Familiarity with data privacy, compliance, and ethical AI considerations.

Preferred Qualifications:

Certifications in AI/ML or Cloud Architecture (e.g., AWS Certified Machine Learning – Specialty).
Experience in product/platform-based AI solutions.
Exposure to NLP, computer vision, or generative AI is a plus.

Machine Learning, AWS, Natural Language Processing, Technical Architect

Preferred Qualifications:

Certifications in AI/ML or Cloud Architecture (e.g., AWS Certified Machine Learning – Specialty).
Experience in product/platform-based AI solutions.
Exposure to NLP, computer vision, or generative AI is a plus.

AIML Architect