Ai/ml Engineer

Description:

AI Architecture & Design Design end to end AI/ML architectures including data ingestion, feature engineering, model development, deployment, monitoring, and retraining.

Define enterprise AI reference architectures for GenAI, ML, NLP, computer vision, and predictive analytics use cases.

Select and standardize AI platforms, frameworks, and tools (e.g., cloud AI services, MLOps platforms, LLM frameworks).

Ensure architectures support scalability, performance, reliability, and security.

AI Strategy & Enablement Partner with business, data, and engineering leaders to translate use cases into technical AI solutions.

Guide AI adoption across ETL, data platforms, and analytics ecosystems.

Establish reusable AI patterns, accelerators, and best practices.

Support POCs and pilots, guiding teams toward production ready solutions.

Data & Platform Integration Collaborate with data architecture teams to ensure data quality, lineage, and governance for AI workloads.

Integrate AI solutions with enterprise data platforms, APIs, and workflows.

Align AI solutions with modern data stacks (cloud, hybrid, streaming, lakehouse).

Responsible AI & Governance Define and enforce Responsible AI principles including fairness, explainability, privacy, bias mitigation, and model transparency.

Implement model governance, validation, approval, and auditability processes.

Ensure compliance with regulatory and risk frameworks (e.g., model risk management, data privacy).

MLOps & AI Operations Design and implement MLOps/LLMOps frameworks for CI/CD, versioning, monitoring, drift detection, and retraining.

Define operational SLAs and production support models for AI systems.

Optimize AI infrastructure cost, performance, and resource utilization.

Leadership & Collaboration Provide architectural leadership and mentorship to data scientists, ML engineers, and platform teams.

Review AI designs and implementations to ensure adherence to standards.

Act as the AI technical advisor for leadership and governance forums.

Primary Skill: Data Scientist

Secondary Skill: DataStage

Tertiary Skill: Node.js

Required Qualifications Education Bachelors or Masters degree in Computer Science, Engineering, Data Science, or a related field.

Experience

• 8+ years in data, analytics, or platform architecture roles.
• 4+ years designing and implementing AI/ML solutions at scale.
• Proven experience deploying AI models into production environments.

Technical Skills

• Strong knowledge of:
• Machine Learning, Deep Learning, and Generative AI concepts
• Python, SQL; familiarity with ML frameworks (TensorFlow, PyTorch, scikit learn)
• Cloud AI platforms (Azure, AWS, GCP)
• MLOps tools and pipelines
• Data engineering and ETL platforms (e.g., Informatica, DataStage, Spark)
• Understanding of APIs, microservices, containers, and orchestration.
• Solid grasp of enterprise architecture principles.
• Experience with AI governance, model risk, and regulatory compliance.
• Familiarity with data security, privacy, and access control patterns.