Job Summary
Job Description
This NLP Engineer role requires end-to-end expertise in designing, developing, deploying, and maintaining machine learning solutions. Responsibilities include data engineering (ETL/ELT pipelines, data cleaning, working with graph and vector databases), model development (training LLMs and deep learning models using Hugging Face, PyTorch, TensorFlow, implementing RAG), MLOps (CI/CD pipelines, model deployment using FastAPI/Flask/gRPC, model monitoring), and API development. The engineer will integrate ML models with LangChain, build scalable solutions on AWS/GCP/Azure, and optimize model performance and security (HIPAA, GDPR compliance).
Must have:
- Strong ML/DL/LLM/NLP background
- Proficiency in Python (PyTorch, TensorFlow)
- Experience with Graph & Vector Databases
- MLOps expertise (MLflow, Kubeflow, SageMaker)
- API development (FastAPI, Flask, gRPC)
- Experience with LangChain and RAG
15 skills required
Job Details
Description
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 |
Key Responsibilities: |
• 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). |
• Model Development & Fine-Tuning: |
• 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. |
• MLOps & Model Deployment: |
• 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. |
• Application & API Development: |
• 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). |
Required Skills: |
• Machine Learning & AI: |
• Strong background in ML, deep learning, LLMs, NLP. |
• Experience with Transformer models (BERT, GPT, T5, LLaMA, etc.). |
• Programming & Development: |
• Proficiency in Python (PyTorch, TensorFlow, Scikit-Learn). |
• Strong experience in APIs and microservices (FastAPI, Flask, gRPC). |
• Data & Infrastructure: |
• Experience with Graph Databases (Neo4j, AWS Neptune). |
• Knowledge of Vector Databases (Weaviate, Pinecone, FAISS). |
• Familiarity with Snowflake, PostgreSQL, NoSQL databases. |
• MLOps & Cloud: |
• 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. |
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