Responsibilities
- Develop and deploy Python-based APIs and microservices for machine learning and AI solutions, ensuring scalability, reliability, and maintainability.
- Collaborate with AI/ML teams to productionize ML models (e.g., regression, classification, NLP, GenAI) and integrate them into business applications.
- Automate model lifecycle management including versioning, deployment, monitoring, and retraining using MLOps best practices.
- Implement and enforce data integrity, normalization, and reconciliation processes, leveraging tools like Great Expectations or custom validation logic.
- Ensure robust testing, monitoring, and observability for all deployed services, including logging, metrics, and alerting.
- Develop and optimize high-performance SQL and NoSQL queries for large-scale data warehousing and transactional systems.
- Champion Agile software engineering practices and participate in code reviews, sprint planning, and continuous integration/deployment processes.
Basic qualifications
- 5+ years of experience in application development using Python, Scala (and/or Java).
- 2+ years of hands-on experience with a public cloud platform (AWS, Azure, or Google Cloud)
- 2+ years of experience with NoSQL databases (e.g., MongoDB) and one relational database (e.g., PostgreSQL, MySQL, Oracle).
- Advanced Python: Deep expertise in Python, memory management, and high-performance libraries for processing multi-gigabyte flat files.
- Data Integrity & Normalization: Experience with financial data types, floating-point arithmetic pitfalls, and building automated reconciliation tools.
- GenAI Ops: Practical experience hosting and querying private LLMs (e.g., GPT, Opus, Llama 3) for code-translation and automation tasks. Experience with MLOps tools (MLflow, Kubeflow, SageMaker, Vertex AI, or similar).
Preferred qualifications
- Experience in creating and deploying AI Agents.
- Familiarity with financial data and regulatory requirements. Strong communication and mentoring skills.
Tags & focus areas
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