Key Responsibilities
- Architect end-to-end AI/ML deployment solutions focusing on AWS services such as S3, Redshift, SageMaker, Bedrock, Lambda, and IAM, ensuring best practices in security, compliance, and scalability.
- Design and integrate data lakes, structured databases, and ML pipelines leveraging Databricks architecture for smooth data processing and AI workload orchestration.
- Lead solution design and orchestration for RPA and GenAI systems, incorporating conversational and agentic AI platforms like Copilot Studio, Agentforce, and Sierra.
- Drive architectural decisions, review technical approaches, and provide leadership to development teams on experimentation, prototyping, and solution delivery.
- Manage model development, deployment, and lifecycle on platforms such as SageMaker and Bedrock, implementing MLOps best practices.
- Implement end-to-end automation of ML pipelines with SageMaker and Databricks workflows, integrating models into scalable, cloud-native services.
- Utilize Kubernetes and container orchestration for scalable AI deployment alongside infrastructure as code tools such as Terraform and CloudFormation.
- Establish advanced monitoring, logging, and performance optimization for AI workloads, promoting high availability and reliability.
- Collaborate with business stakeholders to prioritize AI use cases and ensure architectural alignment with strategic objectives.
- Oversee data ingestion, transformation, and governance workflows to maintain data privacy, quality, and accessibility for AI and RPA workloads.
- Define and implement CI/CD pipelines tailored for AI/ML deployments, ensuring seamless and automated delivery cycles.
- Stay current on emerging AI technologies, especially in Generative AI and LLM domains, and drive integration of innovative solutions with existing platforms.
Required Skills & Qualifications
o 8-10 years of hands-on solutions architecture experience, with significant focus on AI/ML cloud-native architectures and deployments.
o Deep expertise in core AWS services: S3, Redshift, SageMaker, Bedrock, Lambda, IAM, and associated AI services (Comprehend, Rekognition, Lex, Polly, Textract).
o Proven experience architecting data lakes, ETL pipelines, and managing data engineering workflows (AWS S3, Redshift, Databricks).
o Proficiency in advanced Python programming, Jupyter Notebooks, and machine learning libraries such as scikit-learn, TensorFlow, and PyTorch.
o Experience designing and deploying scalable ML models and pipelines, with strong knowledge of MLOps best practices including automation, containerization, and deployment.
o Expertise with Databricks architecture and integration within ML and AI ecosystems.
o Solid understanding of Kubernetes for container orchestration and infrastructure automation using Terraform or CloudFormation.
o Familiarity with conversational AI, agentic AI platforms, and Generative AI workflows, including LLMs and Bedrock.
o Skilled in API design, system integration, and security best practices governing data privacy and compliance.
o Strong business acumen with the ability to translate technical solutions into business value and prioritize use cases accordingly.
o Experience leading technical teams and cross-functional collaboration to drive AI projects from concept through deployment.
o Excellent communication skills and stakeholder management capabilities in Agile environments.
o Proven track record in an IT consulting environment, engaging with large enterprises and MNCs in strategic data solutioning projects.
o Strong stakeholder management, business needs assessment, and change management skills.
- Leadership & Soft Skills:
o Experience managing and mentoring small teams, developing technical skills AI & Advanced Analytics domains.
o Ability to influence and align cross-functional teams and stakeholders.
o Excellent communication, documentation, and presentation skills.
o Strong problem-solving, analytical thinking, and strategic vision.
- Educational Qualifications:
o Bachelor’s or Master’s degree in Computer Science, Engineering, Data Science, or a related quantitative field.
- Preferred Certifications:
o AWS Certified Solutions Architect – Professional
o AWS Certified Machine Learning – Specialty
o Certified Kubernetes Administrator (CKA) or equivalent
o Terraform Associate or equivalent Infrastructure as Code certifications
o Certified Artificial Intelligence Practitioner (CAIP) or similar AI certifications
o Relevant RPA certifications (UiPath, Blue Prism) a plus
