Technical Architect - ML

Job description

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.

If you like wild growth and working with happy, enthusiastic over-achievers, you'll enjoy your career with us!

Originally posted on Himalayas