Senior Machine Learning Engineer

Must have:

• Strong on programming languages like Python, Java
• Should have total 4-7 Yrs of experience in Machine Learning Engineering
• One cloud hands-on experience (GCP preferred)
• Experience working with Dockers
• Environments managing (e.g venv, pip, poetry, etc.)
• Experience with orchestrators like Vertex AI pipelines, Airflow, etc
• Understanding of full ML Cycle end-to-end
• Data engineering, Feature Engineering techniques
• Experience with ML modelling and evaluation metrics
• Experience with Tensorflow, Pytorch or another framework
• Experience with Models monitoring
• Advance SQL knowledge
• Aware of Streaming concepts like Windowing , Late arrival , Triggers etc
• Good to have: Hyperparameter tuning experience.
• Proficient in either Apache Spark or Apache Beam or Apache Flink
• Should have hands-on experience on Distributed computing
• Should have working experience on Data Architecture design
• Should be aware of storage and compute options and when to choose what
• Should have good understanding on Cluster Optimisation/ Pipeline Optimisation strategies
• Should have exposure on GCP tools to develop end to end data pipeline for various scenarios (including ingesting data from traditional data bases as well as integration of API based data sources).
• Should have Business mindset to understand data and how it will be used for BI and Analytics purposes.
• Should have working experience on CI/CD pipelines, Deployment methodologies, Infrastructure as a code (eg. Terraform)
• Good to have, Hands-on experience on Kubernetes
• Good to have Vector based Database like Qdrant
• Good to have: LLM experience (embeddings generation, embeddings indexing, RAG, Agents, etc.).

Experience in Working with GCP tools like:

Storage : CloudSQL , Cloud Storage, Cloud Bigtable, Bigquery, Cloud Spanner, Cloud DataStore, Vector database

Ingest : Pub/Sub, Cloud Functions, AppEngine, Kubernetes Engine, Kafka, Micro services

Schedule : Cloud Composer, Airflow

Processing: Cloud Dataproc, Cloud Dataflow, Apache Spark, Apache Flink

CI/CD : Bitbucket+Jenkinjs / Gitlab ,Infrastructre as a tool : Terraform