Software Engineer delivering MLOps solutions for Generative AI at DataGalaxy. Focusing on reliability and collaboration with product engineering teams in a hybrid environment.
About the role
- Architect designing and implementing MLOps strategy for the EVOKE Phase-2 programme at Quantiphi. Leading enterprise-grade ML pipelines and collaborating across teams for production-ready ML solutions.
Responsibilities
- Architect and implement the MLOps strategy for the EVOKE Phase-2 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, not limited to EVOKE, contributing architectural patterns, best practices, and reusable frameworks.
- Enable observability, monitoring, drift detection, lineage tracking, and auditability across ML/LLM systems.
- 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.
Requirements
- 7-14 years of experience in ML/AI engineering or MLOps roles with strong architecture exposure.
- Strong expertise in AWS cloud-native ML stack , including: EKS (primary), ECS, 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 (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 (Training, Processing, Batch Transform, Pipelines, Feature Store, Model Registry, Model Monitor).
- Experience implementing ML CI/CD pipelines including automated training, testing, validation, model promotion, and endpoint deployment.
- Ability to build dynamic and versioned pipelines using SageMaker Pipelines, Step Functions, or Kubeflow.
- Strong SQL and data transformation experience using Snowflake , Databricks, Spark, or EMR.
- Experience with feature engineering pipelines and Feature Store management (SageMaker or Feast).
- Understanding of lineage tracking : training data snapshot, feature versions, code versioning, metadata tracking, reproducibility.
- Hands-on experience with Bedrock , OpenAI , Anthropic , or Llama models.
- Experience with CloudWatch , SageMaker Model Monitor , Prometheus/Grafana , or Datadog.
- 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).
- Knowledge of Lakehouse (Delta/Iceberg/Hudi) architecture.
- 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.
Benefits
- Flexible work arrangements
- Professional development opportunities
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