Bachelor's or Master's degree in Computer Science, Artificial Intelligence, or related field
3+ years of experience deploying ML models in production
Expert proficiency in Python, including libraries like NumPy, Pandas, and Scikit-learn
Experience with Generative AI stack such as Hugging Face and LangChain
Hands-on experience with MLOps tools like Docker, Kubernetes, and MLflow
Proficiency with cloud platforms like AWS, Azure, or GCP
Strong skills in SQL and NoSQL databases
Responsibilities
Develop, containerize, and deploy ML/AI models in production environments
Implement Large Language Models (LLMs) focusing on prompt engineering and fine-tuning
Automate CI/CD pipelines, model versioning, monitoring, and retraining using MLOps tools
Architect scalable and secure AI infrastructure on cloud platforms
Collaborate on ETL pipelines for data ingestion and preprocessing
Optimize inference engines for low-latency model serving
Ensure compliance with ethical guidelines and implement Explainable AI techniques
Benefits
Opportunity to work on cutting-edge AI/ML technologies
Collaboration with a diverse team of experts in AI
Access to resources for professional development and skill enhancement
Flexible work environment to promote work-life balance
Engagement in responsible AI practices and ethical model development
Full Job Description
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:
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.
Required Technical Skills & Qualifications:
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.
