Job Title: Senior AI/ML Developer
Experience: 6–10 Years Employment Type: Full-Time
Position Summary
We are seeking a highly skilled Senior AI/ML Developer to design, develop, deploy, and optimize enterprise-grade Artificial Intelligence and Machine Learning solutions. The ideal candidate will have hands-on experience with AI/ML frameworks, MLOps, DevOps, and cloud platforms such as AWS, Azure, and GCP. This role involves building scalable machine learning pipelines, deploying production-ready models, and collaborating with cross-functional teams to deliver intelligent, data-driven applications.
Key Responsibilities
- Design, develop, train, and deploy machine learning and deep learning models for enterprise applications.
- Build end-to-end AI/ML solutions using TensorFlow, PyTorch, Keras, and Scikit-learn.
- Develop scalable data preprocessing, feature engineering, and model training pipelines.
- Implement MLOps best practices using MLflow for experiment tracking, model versioning, and lifecycle management.
- Deploy AI/ML models into production using CI/CD pipelines and DevOps practices.
- Design and manage cloud-native AI solutions on AWS, Azure, and GCP.
- Optimize model performance, scalability, and inference latency.
- Collaborate with Data Scientists, Data Engineers, DevOps Engineers, and Product Teams to deliver AI-powered solutions.
- Monitor production models for accuracy, drift detection, and continuous improvement.
- Develop APIs and microservices for AI model serving and integration.
- Maintain documentation, coding standards, and technical best practices.
- Participate in architecture discussions, code reviews, and Agile development processes.
Required Skills
Technical Skills (Must Have)
- Python
- TensorFlow
- PyTorch
- Keras
- Scikit-learn
- MLflow
- AI/ML Frameworks
- Machine Learning
- Deep Learning
- MLOps
- DevOps & CI/CD
- Docker
- Kubernetes
- REST APIs
- Git/GitHub
Cloud Platforms
- AWS
- Microsoft Azure
- Google Cloud Platform (GCP)
Machine Learning Expertise
- Supervised & Unsupervised Learning
- Feature Engineering
- Model Training & Evaluation
- Hyperparameter Tuning
- Model Deployment
- Model Monitoring
- Explainable AI (XAI)
Good to Have
- Generative AI and Large Language Models (LLMs)
- LangChain or LlamaIndex
- Retrieval-Augmented Generation (RAG)
- Vector Databases (Pinecone, FAISS, ChromaDB)
- Apache Spark / PySpark
- Airflow
- NLP and Computer Vision
- Kafka
- Terraform
- FastAPI or Flask
Qualifications