Artificial Intelligence/Machine Learning Engineer

Description :

Key Responsibilities :

ML & AI Pipeline Engineering :

- Build and maintain end-to-end pipelines for machine learning models and GenAI components.

- Implement feature pipelines that transform interaction, QA, sentiment, and operational data into model-ready datasets.

- Operationalize predictive models developed by Data Scientists using Snowflake ML, AWS SageMaker, or equivalent platforms.

- Support batch and nearreal-time inference workflows.

GenAI & RAG Enablement :

- Implement infrastructure and orchestration for RAG-based workflows.

- Integrate LLMs (e.g., Amazon Bedrock) into production pipelines.

- Support retrieval pipelines, embeddings generation, and vector search operations.

- Ensure AI outputs are governed, traceable, and grounded in approved data sources.

Model Deployment & Operations :

- Deploy models and AI services using CI/CD pipelines.

- Implement model versioning, rollback, and environment promotion strategies.

- Monitor model performance, data drift, and pipeline health.

- Partner with DevOps teams to ensure reliability, scalability, and observability.

Security, Governance & Compliance :

- Implement controls to support secure model execution and data access.

- Ensure logging, auditability, and traceability of model predictions.

- Support compliance requirements for regulated environments (e.g., healthcare).

- Participate in model governance and review processes.

Collaboration & Continuous Improvement :

- Collaborate with Data Scientists to productionize models efficiently.

- Work closely with Solution Architects and AI/Prompt Engineers on design decisions.

- Identify opportunities to optimize model performance, cost, and latency.

- Contribute to technical documentation and knowledge transfer.

Required Skills & Experience :

Technical Skills :

- Strong software engineering background with experience in ML systems.

- Proficiency in Python and ML frameworks (e.g., scikit-learn, TensorFlow, PyTorch).

- Experience with cloud ML platforms (AWS SageMaker preferred).

- Familiarity with Snowflake data pipelines and analytics environments.

- Experience with CI/CD, containerization, and infrastructure automation.

Data & AI Skills :

- Experience with feature engineering and data preprocessing.

- Understanding of model lifecycle management.

- Familiarity with GenAI architectures, including RAG and LLM integration.

- Experience working with embeddings, vector databases, or semantic search.

Soft Skills :

- Strong problem-solving and debugging skills.

- Ability to work in cross-functional teams.

- Clear communication and documentation skills.

- Comfort working in iterative, delivery-focused environments.

Nice-to-Have Qualifications :

- Experience in regulated industries (healthcare, insurance, finance).

- Familiarity with model monitoring and evaluation frameworks.

- Experience supporting AI audit or governance processes.

- Exposure to contact center analytics or QA systems.