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.
