AI ML Engineers
Job Description: AI/ML Engineer (2026)
Role Summary:
We are looking for a highly skilled AI/ML Engineer to design, build, and deploy production-grade AI systems. You will bridge the gap between data science and operational software, creating intelligent, scalable, and secure applications using cutting-edge models (LLMs, GenAI) and traditional machine learning. You will focus on turning experimental models into reliable, high-performance, real-world solutions.
Key Responsibilities:
Role Summary:
We are looking for a highly skilled AI/ML Engineer to design, build, and deploy production-grade AI systems. You will bridge the gap between data science and operational software, creating intelligent, scalable, and secure applications using cutting-edge models (LLMs, GenAI) and traditional machine learning. You will focus on turning experimental models into reliable, high-performance, real-world solutions.
Key Responsibilities:
- Productionize ML/AI Models: Develop, containerize, and deploy machine learning models, including Deep Learning and GenAI, into production environments.
- Generative AI & LLMs: Implement Large Language Models (LLMs) using frameworks like LangChain/LlamaIndex, focusing on prompt engineering, RAG architectures, and fine-tuning.
- MLOps Implementation: Automate CI/CD pipelines, model versioning (DVC), monitoring, and retrain pipelines using MLOps tools (MLflow, Kubeflow).
- System Architecture: Architect scalable, resilient, and secure AI infrastructure on cloud platforms (AWS, Azure, or GCP).
- Data Engineering: Collaborate on ETL pipelines to ensure high-quality data ingestion, preprocessing, and feature engineering for model training.
- Model Optimization: Optimize inference engines (e.g., Triton, vLLM) for low-latency, high-performance model serving.
- Responsible AI: Ensure models are compliant with ethical guidelines, auditing for bias, and implementing Explainable AI (XAI) techniques.
- Education: Bachelor's or Master's degree in Computer Science, Artificial Intelligence, or a related technical field.
- Experience: 3+ years of experience in deploying ML models in production.
- Programming: Expert proficiency in Python (including libraries: NumPy, Pandas, Scikit-learn, PyTorch/TensorFlow).
- GenAI Stack: Experience with Hugging Face, LangChain, vector databases (Pinecone, Milvus), and LLM APIs.
- MLOps & Tools: Hands-on experience with Docker, Kubernetes, MLflow, and CI/CD tools.
- Cloud Platforms: Proficiency with AWS SageMaker, Azure ML, or Vertex AI.
- Database Knowledge: Strong skills in SQL and NoSQL databases.
