Saama Technologies Inc Careers

We are seeking a Engineer with end-to-end expertise in designing, developing, deploying, and maintaining machine learning solutions. This role requires a hands-on engineer who can work across data engineering, model development, MLOps, and API integration to build scalable AI-driven applications.

 

Location – Remote

 

• Data Engineering & Processing:

• Design and implement scalable ETL/ELT pipelines for structured and unstructured data.

• Clean, normalize, and preprocess data for ML models.

• Work with Graph Databases (Neo4j, AWS Neptune) and Vector Databases (Weaviate, Pinecone, FAISS).

• Train and fine-tune LLMs and deep learning models using Hugging Face, PyTorch, TensorFlow.

• Implement retrieval-augmented generation (RAG) for knowledge-based AI.

• Optimize model performance for efficiency and scalability.

• Develop CI/CD pipelines for ML model training and deployment using MLflow, Kubeflow, SageMaker.

• Deploy models as APIs using FastAPI, Flask, or gRPC.

• Automate model monitoring, drift detection, and retraining.

• Build APIs and microservices for AI applications.

• Integrate ML models with LangChain for AI-powered applications.

• Implement scalable solutions in AWS, GCP, or Azure.

• Performance Optimization & Security:

• Optimize model inference speed and reduce cloud costs.

• Ensure data security and compliance (HIPAA, GDPR where applicable).

• Machine Learning & AI:

• Strong background in ML, deep learning, LLMs, NLP.

• Experience with Transformer models (BERT, GPT, T5, LLaMA, etc.).

• Proficiency in Python (PyTorch, TensorFlow, Scikit-Learn).

• Strong experience in APIs and microservices (FastAPI, Flask, gRPC).

• Experience with Graph Databases (Neo4j, AWS Neptune).

• Knowledge of Vector Databases (Weaviate, Pinecone, FAISS).

• Familiarity with Snowflake, PostgreSQL, NoSQL databases.

• Experience with MLflow, Kubeflow, SageMaker for model lifecycle management.

• Proficiency in Docker, Kubernetes, Terraform.

• Strong understanding of AWS/GCP/Azure services.

• AI Application Development:

• Experience integrating AI models with LangChain for intelligent applications.

• Understanding of retrieval-augmented generation (RAG) workflows.