Bachelor's degree in Computer Science, Data Science, AI/ML, or a related technical field. Equivalent practical experience or certifications will also be considered.
2-3 years of experience in presales, solution architecture, or technical consulting, with a focus on AI/ML initiatives, data platforms, or analytics-driven solutions.
Experience designing or supporting AI workflows, data pipelines, or ML model deployment scenarios.
Proficiency in designing AI/ML-powered solutions, including model training pipelines, inference workflows, and data preprocessing architectures.
Familiarity with AI/ML frameworks such as TensorFlow, PyTorch, Scikit-learn, or MLflow, and experience integrating them into enterprise or production settings.
Hands-on understanding of data engineering, including streaming data processing, ETL/ELT workflows, and orchestration tools.
Knowledge of cloud-native development and deployment using platforms such as AWS Sagemaker, Google Vertex AI, Azure ML, or custom containerized ML environments.
Ability to present end-to-end AI solutions that combine data ingestion, model building, MLOps, and business intelligence layers.
Strong grasp of Kafka, Spark, Hadoop, and distributed systems that support real-time or batch data workloads for AI use cases.
Understanding of DevOps/MLOps practices, including CI/CD for ML, containerization (Docker), and model versioning.
Exposure to data governance, model explainability, ethical AI principles, and regulatory alignment (e.g., GDPR, PII handling).
Strong communication skills to convey complex AI/ML concepts to both technical and non-technical stakeholders.
Certifications in cloud data/AI (e.g., AWS Certified Machine Learning, Google Professional Data Engineer) are a strong plus.
Engage with clients to identify opportunities for applying AI/ML, translating business goals into technical solution designs.
Craft and deliver technical proposals, PoCs, and solution blueprints that showcase AI/ML capabilities integrated with modern data infrastructure.
Design scalable data ingestion and model deployment strategies, highlighting performance, reliability, and maintainability.
Collaborate with sales, product, and engineering teams to align client needs with AI/ML use cases and delivery capabilities.
Guide clients through feasibility assessment, model lifecycle planning, and AI readiness evaluation.
Support the integration of AI into existing systems, whether through cloud platforms, APIs, or on-prem inference engines.
Stay current with the AI/ML landscape-including generative AI, foundation models, AutoML, and vector databases-and incorporate relevant innovations into solutioning.
Document solution architectures, data flow diagrams, and ML lifecycle strategies to support handover and implementation phases.
Promote best practices in MLOps, explainability, and responsible AI to ensure secure, ethical, and future-proof AI adoption.
