BPK Tech - MLOps Engineer - AWS Platform (4-9 yrs)

Job Description :

- 5+ years of experience in relevant fields (internships, prototypes, and personal projects not counted).

- Coding skills required (preferably Python or Java).

- Experience in DevOps culture (own development-deployment end-to-end).

Experience in building or deploying solutions in the cloud, either :

- Cloud Native (Serverless) : S3, Lambda, AWS Batch, ECS.

- Cloud Agnostic : Kubernetes services.

- CICD experience : GitHub Actions or Jenkins.

- Infrastructure as code : Terraform.

Expertise in at least one of the following focus areas :

MLOps : ML deployment and model serving (e.g., Kubeflow, KNative, KServe, Nvidia Triton, Ray Serve, MLflow, Sagemaker).

Graph Databases : Ingesting and consuming data in graph databases such as Neo4J, JanusGraph, or DGraph.

- Big Data : Building big data pipelines/platforms to process petabytes of data (Kafka, Debizium, Lakehouse, Data Catalog, Data Lineage, Spark, Glue, EMR).

- ML (Platform) Engineering : Building ML platforms or ML solutions (Kafka, Sagemaker, KServe).

Note : High preference for MLOps and Kafka expertise.

Job Responsibilities :

- Design, develop, and launch strategic machine learning solutions, driving business-wide innovation utilizing AI and analytical techniques at scale.

- Take full ownership of the end-to-end software development lifecycle, encompassing design, testing, deployment, and operations, engage in technical discussions and strategy, and participate hands-on in design reviews, code reviews, and implementation.

- Craft high-performance, production-ready machine learning code for our next-generation real-time ML platform. Extend existing ML libraries and frameworks.

- Working closely with other engineers and scientists, develop solutions to accelerate model development, validation and experimentation cycles, and integrate models and algorithms in production systems at a very large scale.

- Uphold the highest standards of technical rigor in engineering and operational excellence, build highly resilient and scalable systems, and champion operational and process improvements.