The Senior Data Science and AI Engineer designs, develops, and deploys advanced machine learning, deep learning, and Generative AI systems into production environments. The role transforms raw and curated enterprise data into scalable, secure, and automated AI solutions, including LLM-powered applications (e.g., retrieval-augmented generation, vector search, and agentic workflows). The position provides technical leadership through architecture guidance, code reviews, and mentoring, and partners with business, data engineering, and platform teams to ensure end-to-end delivery, reliability, and measurable business impact.

Responsibilities

1. Design and develop ML/DL models and AI systems
   Design, train, and evaluate supervised/unsupervised ML and deep learning models (e.g., NLP, classification, forecasting, anomaly detection, computer vision where applicable). Select model architectures, loss functions, and evaluation metrics aligned with business goals and data characteristics.
2. Build and deploy Generative AI / LLM solutions
   Develop LLM-powered applications, including prompt engineering, RAG pipelines, embeddings, vector database indexing/search, and orchestration for agentic workflows. Implement guardrails and safety controls, and tune solutions for latency, accuracy, and cost.
3. Productionization, MLOps, and monitoring
   Package, containerize, and deploy models and AI services using CI/CD practices. Implement model versioning, experiment tracking, model registry, automated retraining triggers (as needed), and production monitoring (drift, latency, throughput, and quality). Maintain SLAs/SLOs and incident response playbooks for AI services.
4. Data pipelines and feature engineering
   Partner with data engineering to design and optimize data ingestion, transformation, and feature pipelines for large-scale datasets. Develop robust data validation checks, schema management, and reproducible feature generation using SQL and distributed compute platforms.
5. Performance optimization and responsible AI
   Conduct model diagnostics and error analysis; improve performance via feature selection, hyperparameter tuning, calibration, and model compression/acceleration where appropriate. Implement bias/fairness checks, explainability methods, and documentation to support responsible AI practices.

Typical Deliverables

• Production ML/LLM services (APIs, batch scoring, streaming inference as applicable)
• RAG/agentic workflows with vector search and retrieval evaluation
• Experiment tracking artifacts, model cards, technical documentation, runbooks
• Monitoring dashboards and alerting for model/data drift and service reliability
• Data/feature pipelines and automated validation checks

Tools, Technologies, and Platforms

• Programming / Query: Python, SQL (Java/R optional)
• ML/DL Frameworks: PyTorch, TensorFlow, scikit-learn; Hugging Face ecosystem
• GenAI Stack: LLM APIs, embeddings, RAG patterns, LangChain-style orchestration (or equivalent), vector databases (e.g., pgvector, Pinecone, Weaviate, Milvus, OpenSearch/Elasticsearch vector search)
• Data Platforms: Databricks/Spark, distributed compute, data lake/warehouse tools
• Cloud & DevOps/MLOps: AWS/Azure/GCP, MLflow (or equivalent), Docker, Kubernetes, CI/CD pipelines, Git-based workflows
• Datastores: SQL/NoSQL databases; object storage; message queues (as applicable)

Minimum Education and Experience Requirements

• Education: Master’s degree (or higher) in Computer Science, Data Science, Statistics, Mathematics, Engineering, or a closely related quantitative field.
• Experience: At least 10 years of progressive experience in data science/ML engineering/AI engineering (or equivalent), including experience deploying ML models into production.

• Advanced proficiency in Python and SQL for data processing, feature engineering, and model development
• Strong understanding of machine learning algorithms, statistics, and model evaluation techniques
• Hands-on experience building, deploying, and maintaining production ML systems (APIs and/or batch pipelines)
• Experience implementing LLM/RAG solutions, embeddings, and vector search workflows
• Experience with cloud environments (AWS/Azure/GCP) and modern MLOps practices (versioning, monitoring, CI/CD)
• Ability to produce technical documentation and communicate effectively with cross-functional stakeholders