Data Scientists – AI/ML Engineer (12+ experience )

CloudIngest is seeking a Data Scientist with expertise in AI and Machine Learning. The role involves building end-to-end machine learning models, fine-tuning large language models, and developing scalable data pipelines while utilizing various tools and frameworks.

Responsibilities:

• Strong experience with Python and ML frameworks (TensorFlow, PyTorch, Scikit-learn)
• End-to-end model building: data prep, training, evaluation, deployment
• Experience with NLP, embeddings, transformer architectures, and LLM fine-tuning
• Fine-tuning or prompting GPT-based LLMs
• Experience building RAG systems (Retrieval-Augmented Generation)
• Knowledge of vector databases
• Understanding of agentic frameworks (CrewAI, LangChain agents, AutoGen, etc.)
• Developing multi-agent systems with tools, memory, and planning loops
• Airflow for workflow orchestration
• Docker for containerization
• Kubernetes for scalable deployment
• CI/CD for model deployment
• Model monitoring and drift detection
• API development (FastAPI, Flask)
• Experience with SQL and NoSQL data stores
• Must have experience in:
• Palantir Foundry (data ingestion, transformations, ontology modeling, analytics workflows)
• Snowflake (advanced SQL, performance tuning, analytical schema design)
• Building scalable ETL/ELT data pipelines (batch, near real-time)
• Data modeling for analytics and reporting (layers)
• Working with relational and NoSQL databases
• Python for data processing and automation
• Data quality, governance, and lineage practices
• Unsupervised clustering and classification of structured data in DBMS/files and unstructured data
• Creating multi-stage analysis/transformation pipelines like Contour in Palantir Foundry
• Distributed data processing frameworks like Spark
• Querying using GraphQL, Scalding, etc
• Create apps, reports, dashboards like those in Palantir Foundry
• Good to have:
• Dbt, Airflow, Spark or similar orchestration/processing frameworks
• BI & visualization tools (Tableau, Power BI, Looker, etc.)
• Streaming data platforms (Kafka/Kinesis)
• Cloud platforms (AWS/Azure/GCP)
• ML feature engineering or analytics support
• Agentic AI skills

Requirements:

• Strong experience with Python and ML frameworks (TensorFlow, PyTorch, Scikit-learn)
• End-to-end model building: data prep, training, evaluation, deployment
• Experience with NLP, embeddings, transformer architectures, and LLM fine-tuning
• Fine-tuning or prompting GPT-based LLMs
• Experience building RAG systems (Retrieval-Augmented Generation)
• Knowledge of vector databases
• Understanding of agentic frameworks (CrewAI, LangChain agents, AutoGen, etc.)
• Developing multi-agent systems with tools, memory, and planning loops
• Airflow for workflow orchestration
• Docker for containerization
• Kubernetes for scalable deployment
• CI/CD for model deployment
• Model monitoring and drift detection
• API development (FastAPI, Flask)
• Experience with SQL and NoSQL data stores
• Must have experience in Palantir Foundry (data ingestion, transformations, ontology modeling, analytics workflows)
• Snowflake (advanced SQL, performance tuning, analytical schema design)
• Building scalable ETL/ELT data pipelines (batch, near real-time)
• Data modeling for analytics and reporting (layers)
• Working with relational and NoSQL databases
• Python for data processing and automation
• Data quality, governance, and lineage practices
• Unsupervised clustering and classification of structured data in DBMS/files and unstructured data
• Creating multi-stage analysis/transformation pipelines like Contour in Palantir Foundry
• Distributed data processing frameworks like Spark
• Querying using GraphQL, Scalding, etc
• Create apps, reports, dashboards like those in Palantir Foundry
• dbt, Airflow, Spark or similar orchestration/processing frameworks
• BI & visualization tools (Tableau, Power BI, Looker, etc.)
• Streaming data platforms (Kafka/Kinesis)
• Cloud platforms (AWS/Azure/GCP)
• ML feature engineering or analytics support
• Agentic AI skills