Must have skills & Qualifications:

8+ years working in ML/AI engineering or MLOps roles with strong architecture exposure.
Strong expertise in AWS cloud-native ML stack, including: SageMaker(primary), EKS, Lambda, API Gateway, CI/CD (CodeBuild/CodePipeline or equivalent)
Hands-on experience with at least one major MLOps toolset and awareness of alternatives: MLflow, Kubeflow, SageMaker Pipelines, Airflow, BentoML, KServe, Seldon.
Deep understanding of model lifecycle management (feature engineering->training registry deployment monitoring).
Experience implementing or supporting LLMOps pipelines, including: prompt versioning, evaluation metrics, automation frameworks
Deep understanding of ML lifecycle: data ingestion, feature engineering, training, evaluation, model packaging, CI/CD, drift detection, monitoring, and governance.
Strong experience with AWS SageMaker (Pipelines, Feature Store, Model Registry, Model Monitor).
Experience implementing ML CI/CD pipelines including automated training, testing, validation, model promotion, and endpoint deployment.
Experience working on Infrastructure as Code (IaC) tools and CI/CD pipelines
Experience with Kubernetes based development
Experience with feature engineering pipelines and Feature Store management.
Understanding of lineage tracking: training data snapshot, feature versions, code versioning, metadata tracking, reproducibility.
Hands-on experience with AWS Bedrock and Agentcore service
Experience with CloudWatch, SageMaker Model Monitor, Prometheus/Grafana.
Strong foundation in Python and cloud-native development patterns.
Solid understanding of security best practices, IAM, secrets management, and artifact governance.

Good to have skills:

Experience with vector databases, RAG pipelines, or multi-agent AI systems.
Exposure to DevOps and infrastructure-as-code (Terraform, Helm, CDK).
Hands-on understanding of model drift detection, A/B testing, canary rollouts, and blue-green deployments.
Familiarity with Observability stacks (Prometheus, Grafana, CloudWatch, OpenTelemetry).
SQL and data transformation experience using Snowflake, Databricks, Spark.
Ability to translate business goals into scalable AI/ML platform designs.
Strong communication and cross-team collaboration skills.
Ability to guide engineering teams through technical uncertainty and design choices.

Key Responsibilities:

Architect and implement the MLOps strategy for the programme, ensuring alignment with the project proposal and delivery roadmap.
Design and own enterprise-grade ML/LLM pipelines covering model training, validation, deployment, versioning, monitoring, and CI/CD automation.
Build container-oriented ML platforms (EKS-first) while evaluating alternative orchestration tools with similar capabilities (Kubeflow, SageMaker, MLflow, Airflow, etc.).
Implement hybrid MLOps + LLMOps workflows, including prompt/version governance, evaluation frameworks, and monitoring for LLM-based systems.
Serve as a technical authority across multiple internal and customer projects, contributing architectural patterns, best practices, and reusable frameworks.
Enable observability, monitoring, drift detection, lineage tracking, and auditability across ML/LLM systems.
Define and implement standards for model deployment, monitoring, governance, and automation to ensure production-grade reliability and scalability.
Collaborate with cross-functional teams data engineering, platform, DevOps, and client stakeholders to deliver production-ready ML solutions.
Ensure all solutions adhere to security, governance, and compliance expectations, particularly around handling cloud services, Kubernetes workloads, and MLOps tools.
Conduct architecture reviews, troubleshoot complex ML system issues, and guide teams through implementation across cloud-native ML platforms.
Mentor engineers and provide guidance on modern MLOps tools, platform capabilities, and best practices.

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