Applied AI/ML Senior Associate

Required qualifications, capabilities, and skills

• Formal training or certification in software engineering concepts, plus 5+ years of applied experience building production Python systems, including web/API services (Flask or FastAPI) and the ML/NLP ecosystem (scikit-learn, pandas, NumPy, spaCy, PyTorch or TensorFlow).

• Demonstrated experience taking machine learning models from prototype to production — training, packaging, deployment, monitoring, retraining, and decommissioning — in real-world business applications.

• Proven experience working with large datasets and distributed compute (Spark / Databricks or equivalent), with SQL fluency and an understanding of partitioning, performance, and cost.

• Hands-on practical experience across system design, application development, testing, and operational stability for services that run on a daily production schedule.

• Experience developing, debugging, and maintaining code in a large corporate environment, using modern programming languages and database query languages, with disciplined use of version control and code review.

• Working knowledge of LLM application patterns — prompt design, retrieval-augmented generation (RAG), embeddings and vector search, structured output, and tool/function calling.

• Experience with agentic AI frameworks — multi-agent orchestration, planning and tool use, and integration patterns such as the Model Context Protocol (MCP); familiarity with Google ADK, Arize Phoenix SDK, Claude skills is a must.

• Overall knowledge of the Software Development Life Cycle, and a solid understanding of agile delivery practices including CI/CD, Application Resiliency, and Security.

Preferred qualifications, capabilities, and skills

• Production experience with Databricks (Delta Lake, Unity Catalog, MLflow, Databricks Jobs) and workflow orchestration with Apache Airflow and strong working knowledge of both supervised and unsupervised techniques, including tree-based and kernel methods (gradient boosting, random forest, SVM) and anomaly-detection approaches such as Isolation Forest, Local Outlier Factor, and locality-sensitive hashing.

• Solid grounding in data pre-processing, feature engineering, model selection, hyper-parameter tuning, and evaluation, including choosing appropriate metrics for imbalanced and unlabeled problems.

• Applied NLP experience — text-to-SQL / natural-language query, entity extraction and linking, relevancy ranking, summarization, and news or research feed analytics; exposure to computer vision or multi-modal document processing (OCR, layout-aware extraction) is a plus and familiarity with AWS machine learning services such as Amazon Bedrock with EKS-based deployment.

• Knowledge of deep learning architectures (CNNs, transformers, sequence models) and of reinforcement learning concepts and their practical applications.

• Experience building self-service ML tooling — model catalogues, automated pipelines, feature stores, and explain ability (SHAP, LIME) surfaced to non-technical users with understanding of industry-standard validation and testing for LLMs — ground-truth evaluation datasets, accuracy, hallucination and toxicity metrics, guardrails and content moderation, red-teaming, and embedding evals into CI/CD.

• Experience with AI/ML observability (OpenTelemetry, Phoenix, or equivalent tracing) and with model governance, auditability, and control requirements in a regulated financial-services environment and familiarity with financial risk domain concepts — market, credit, counterparty, or investment risk, portfolio exposure, and data-quality controls.