While technology is the heart of our business, a global and diverse culture is the heart of our success. We love our people and we take pride in catering them to a culture built on transparency, diversity, integrity, learning and growth.


If working in an environment that encourages you to innovate and excel, not just in professional but personal life, interests you- you would enjoy your career with Quantiphi!

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.