Note: The job is a remote job and is open to candidates in USA. Arkhya Tech. Inc. is seeking an experienced ML Ops & Data Platform Engineer to support projects spanning machine learning operations and shared data platform engineering. The role will design, build, and operate AWS-native ML infrastructure and an S3-based data lakehouse, including infrastructure as code, CI/CD, ingestion pipelines, governance, observability, documentation, and knowledge transfer.
Responsibilities
- Design, build, and operate AWS-native ML infrastructure, as part of a team, supporting batch and real-time inference workloads, including: Amazon SageMaker (training jobs, endpoints, pipelines, model registry) ECS/EKS + Docker for containerized services
- AWS Lambda and Step Functions for orchestration and event-driven workflows S3 as the primary data/artifact layer, with lifecycle and access policies CloudWatch (and related tooling) for logging, metrics, and alerting IAM & VPC design following least-privilege and network security best practices Infrastructure as Code via AWS CDK or CloudFormation
- Implement CI/CD pipelines for ML (data, model, and code), including automated testing, packaging, and promotion of models across dev/staging/production environments
- Help establish model and data versioning, experiment tracking, and lineage for ML pipelines to support reproducibility and auditability (builds on the shared platform's catalog and lineage rather than duplicating it)
- Build monitoring, logging, and alerting for model performance, model-input data drift, and system health; help define SLOs/SLAs for critical ML services and build the automation needed to meet them
- Collaborate with data scientists and software/platform engineering teams to translate experimental workflows into production-grade services
- Contribute to the architecture and hands-on build of an S3-based data lake / lakehouse for the Science Office, including zone design (raw/curated/consumption), open table formats (e.g., Apache Iceberg), partitioning, and lifecycle policies
- Build batch and streaming ingestion/ETL/ELT pipelines using AWS Glue (jobs, crawlers)
- Implement data cataloging, governance, and access control, in collaboration with governance/security stakeholders, using the Glue Data Catalog and Lake Formation (finegrained, cross-team permissions), and optionally DataZone for data product publishing/discovery across science teams
- Enable self-service analytics and data access for scientists across the Science Office via Athena including query patterns, cost controls, and onboarding documentation
- Help establish data quality, schema/metadata management, and lineage frameworks (e.g., Glue Data Quality, schema registries) for shared datasets — distinct from ML-side model-input drift monitoring, this workstream covers dataset-level quality for assets consumed across multiple teams
- Identify and implement cost optimizations across both ML and data platform workloads (right-sizing, spot/scheduling strategies, storage tiering, Athena/Redshift query cost controls) without compromising reliability
- Collaborate with data scientists, analysts, and scientists across the broader Science Office to translate experimental workflows and analytical needs into production-grade services
- Document architecture, runbooks, and operational procedures for both the ML infrastructure and the data platform, and provide knowledge transfer to internal teams as part of engagement close-out
Skills
- 5+ years of hands-on experience in MLOps, data engineering, DevOps, or cloud infrastructure engineering roles, with a strong recent focus on AWS
- Demonstrated production experience with a substantial subset of ML infrastructure services: SageMaker, ECS/EKS, Lambda, Step Functions, S3, CloudWatch, IAM, VPC, and CDK or CloudFormation for infrastructure as code
- Demonstrated production experience with a substantial subset of AWS data platform services: Glue, Lake Formation, Athena
- Experience designing and operating data lake/lakehouse architectures on S3, including ETL/ELT pipeline development and open table formats (e.g., Iceberg)
- Experience with data governance, cataloging, and multi-team access control (Lake Formation, Glue Data Catalog, or equivalent) for shared, regulated data assets
- Strong Python and SQL skills, with working familiarity with at least one ML framework (e.g., TensorFlow, PyTorch, scikit-learn) sufficient to integrate with and support data science workflows
- Experience with containerization and orchestration (Docker, Kubernetes) in production settings
- Experience building and operating CI/CD pipelines for ML and data systems (data, model, and code promotion)
- Must be authorized to work in the country where work will be performed
- AWS certifications (Solutions Architect – Professional, Machine Learning – Specialty, Data Analytics/Data Engineer) or equivalent demonstrated expertise
- Experience with multi-account AWS architectures and GPU workload management on AWS
- Experience with MLflow, SageMaker Model Registry, or similar model governance tooling
- Experience with DataZone or data-mesh/data-product patterns
- Experience with dbt, Airflow/MWAA, or similar transformation/orchestration tooling
- Experience standing up self-service data platforms consumed by non-engineering scientific/analytical users
- Terraform experience as an alternative/complement to CDK
- Prior experience delivering as a contractor/consultant with clean documentation and handoff practices
- Prior experience in healthcare, life sciences, or other regulated environments
Benefits
- USA (Remote) PST
Company Overview