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.
• 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.