Job Description:

Proven experience as a Machine Learning Engineer or similar role. Overall 10 Years
Expert level experience in ML SDLC, developing and productionizing Python and Java
applications
Expert level hands on experience in deploying ML applications to AWS cloud using (SageMaker,
EMR, S3, VPC endpoint etc.)
Hands on experience in AWS apps such EMR, Sage Maker , Cloud Watch, S3 Data Lake etc. ((this is
a must)
Strong knowledge in CI/CD pipelines and tools such as Jenkins, Spinnaker, Bitbucket, Splunk,
CloudWatch, Grafana, Dynatrace, Terraform .. etc. (this is a must)
Experience in deploying applications Kubernetes and AWS platform.
Familiarity with machine learning frameworks (like Keras or PyTorch) and libraries (like
scikit-learn)
Familiarity with data pipelines, HADOOP, Hive, Redshift etc.
AWS certification (Developer or Architect or ML Specialty) is a huge plus.
Experience deploying and scaling distributed systems in a cloud environment (preferably AWS
implementations)
Advanced knowledge of architecture and design across all systems and cloud computing
environments
Strong Programming skills in Python, Bash, Groovy and software engineering principles.
Develop high quality, secure, scalable software solutions based on technical requirements
specifications 
Experience in LLM and Generative AI
Experience in Document extraction/chat
Strong experience in Python, NLP
Experience working in a cloud-native environment such as AWS
Should have hands on experience with AWS Neptune or Neo4J graph database
Experience in building and maintaining open-domain or health care domain-specific ontologies
Understanding of knowledge graphs
Have experience in building graph-based ontology from scratch and working with structured and
unstructured data
Experience supporting ML models development on big data infrastructure (on knowledge graph
would be a bonus)
Hands on python to build knowledge Graph/ontologies.
Experience with AWS Textract, Comprehend Medical (nice to have