• Scale Addi’s competitive advantage by building a world-class ML Ops foundation that accelerates the transition from model prototype to production, while ensuring our AI systems from credit scoring to generative agents are resilient, cost-efficient, and seamlessly integrated into our core financial product. • Ensure ML/AI systems can be served reliably in production, maintaining strong operational excellence for availability, latency, and incident response, in partnership with the Data Scientist role for model/agent logic and iteration. • Build and maintain the serving and integration layer for ML/AI solutions (APIs, connectors, asynchronous execution patterns), enabling seamless integration with internal systems and Ops tooling. • Establish clear mechanisms for monitoring and reliability of ML/AI systems in production (dashboards, alerts, core KPIs, regression detection, and data/feature quality checks). • Enable repeatable delivery for ML/AI services through strong engineering practices (CI/CD, testing, release strategies, rollback, and operational runbooks). • Make contributions to our Architecture Decision Records repository by evaluating and proposing platform upgrades for ML/AI systems (e.g., feature serving patterns, workflow orchestration, scalable storage) to improve reliability, scalability, and reuse.

• Build the end-to-end pipeline: recording → transcription → summarization → coaching report • Auto-generate Pre-Diagnosis Reports from survey responses and startup data • Design expert matching algorithms using startup profiles and diagnosis results • Architect and implement Multi-Agent systems • Fine-tune LLMs and train domain-specific models • Design prompts, build evaluation frameworks, manage model versions, and run A/B tests

• Architect & ship: Design end-to-end Python services (batch + streaming) that ingest model outputs, run constrained optimization, and surface real-time dispatch decisions. • Model & simulate: Build/extend ML models (gradient-boosting, deep learning, OR-Tools) and run time-horizon simulations to quantify cost vs. service-level trade-offs. • Operationalize: Automate training, validation, A/B rollout, and monitoring (SageMaker / Airflow). • Lead & collaborate: Partner with Product, Ops, and Data Engineering; mentor a small squad of ML engineers; present findings to execs. • Continuously improve: Instrument NPS / cost telemetry, identify failure modes, and iterate.

• Work across diverse GenAI platforms like AWS, Salesforce, Oracle, Snowflake, MS Copilot, and other 3rd party GenAI platforms and libraries. • Automate workflows involving extraction of complex, multimodal unstructured content from variety of sources in to highly accurate and reliable structured content using platforms like AWS Textract and Bedrock • Design and build MCP hosts, clients and servers • Establish and use frameworks for automated LLM testing • Create regression test suites to detect drift or prompt breakage • Integrate with internal and external web services using secure authentication and authorization mechanisms • Adopt and ensure safe practices to protect against prompt injections, jailbreaks, and conform to enterprise security guidelines • Design, develop, and deploy production-grade traditional ML models (e.g., regression, classification, clustering, recommender systems) for a variety of business use cases. • Design, maintain, and optimize end-to-end AI/ML pipelines including data ingestion, training, evaluation, deployment, and monitoring on cloud infrastructure (e.g., AWS or equivalent) • Ensure AI/ML solutions are scalable, reliable, secure, and cost-effective within cloud environments • Create reusable components, frameworks, and best practices to accelerate AI development • Partner with data scientists, architects, product managers, business stakeholders and technical teams across organization to align AI solutions with organizational goals. • Provide hands-on technical support and mentorship to technical teams across the enterprise.