Machine Learning Engineer designing and optimizing deep learning models for safety - critical environments at Destinus. Shaping the future of high - speed, autonomous flight technologies.
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
Job title
Job type
Experience level
Salary
Not specified
Degree requirement
Tech skills
Location requirements
Machine Learning Engineer optimizing personalization systems for Spotify's audio streaming service. Collaborating with cross - functional teams to enhance user experience and deliver recommendations.
Principal Machine Learning Engineer developing ML and GenAI solutions in a cloud - native environment at Flexera. Leading a high - impact team and driving operational excellence for ML infrastructure.
Senior ML Platform/Ops Engineer building ML systems for AI - powered learning at Preply. Productionizing machine learning with high reliability, performance, and observability in a hybrid environment.
Senior ML Platform/Ops Engineer building AI - powered ML pipelines for a dynamic Ed - Tech company. Collaborating with ML scientists and engineers to ensure reliable deployment and observability.
Senior ML Platform/Ops Engineer at Preply building and maintaining ML pipelines, collaborating with ML Scientists and Data Engineers.
Machine Learning Engineer developing advanced Deep Learning models for autonomous driving technology at Mobileye. Collaborating in a high - end algorithmic engineering team on critical computer vision challenges.
Machine Learning Engineer focusing on vulnerabilities and security of AI systems at Carnegie Mellon University. Collaborating with a team to build robust prototypes and provide solutions for government sponsors.
Lead machine learning engineer developing solutions for Army enterprise AI and ML team. Collaborating with experts to deliver cutting - edge analytics and models for real - world challenges.
Machine Learning & Signal Processing Scientist at BlueGreen Water Technologies analyzing multi - source environmental data. Focused on developing algorithms and models for signal processing and machine learning techniques.