AI/ML Engineer - Python

Key Skills :

- AI

- Artificial Intelligence

- AI/ML

- Python

- FastAPI

- Machine Learning

- NLP

- LLM

- Docker

- Kubernetes

- AWS

- Microservices

- REST APIs

- Vector Databases

Roles & Responsibilities :

- Design, develop, and deploy AI/ML applications and intelligent solutions.

- Build and maintain scalable pipelines for containerized applications using EKS and microservices architecture.

- Optimize model inference time and operational costs while exposing model inference via REST/gRPC APIs.

- Implement observability, monitoring, and FinOps practices for efficient resource utilization.

- Develop high-quality Python code following best practices such as TDD, concurrency, and multi-threading.

- Develop APIs using FastAPI and integrate AI models into scalable microservices architectures.

- Implement event-driven architectures using message brokers such as Kafka and RabbitMQ.

- Manage containerized applications using Docker, Kubernetes, and Helm.

- Utilize LLM frameworks such as Hugging Face Transformers and LangChain for AI solution development.

- Implement Retrieval-Augmented Generation (RAG) pipelines and vector databases like Qdrant and ChromaDB.

- Optimize model training, serving, caching, and evaluation pipelines using PyTorch/TensorFlow and MLFlow/Airflow.

- Deploy and manage AI solutions on AWS cloud services.

- Ensure secure handling of sensitive data (PII/PHI) with encryption and compliance standards.

- Manage authentication and authorization mechanisms including RBAC.

- Work with SQL and NoSQL databases such as MongoDB, DocDB, and Snowflake.

- Implement logging, monitoring, and observability tools for continuous system performance tracking.

Experience Required :

- 4 to 8 years of experience in AI/ML development and deployment.

- Strong experience in Python programming and AI/ML frameworks.

- Hands-on experience with Machine Learning, NLP, and LLM-based applications.

- Experience in building scalable APIs using FastAPI or similar frameworks.

- Experience with containerization and orchestration tools such as Docker and Kubernetes.

- Knowledge of microservices architecture and event-driven systems.

- Experience with vector databases and RAG architectures is preferred.

- Experience working with cloud platforms, preferably AWS.

- Familiarity with CI/CD pipelines and MLOps practices.

- Strong problem-solving skills and ability to work in agile development environments.