Ai Ml Engineer

About the Role

We are looking for an experienced AI Engineer to design, develop, and deploy AI/ML-based solutions that power our products and internal systems. You will collaborate with data scientists, software engineers, and product teams to build scalable and production-ready AI applications.

Key Responsibilities

• Design, develop, and deploy machine learning and deep learning models for production environments.
• Work with large-scale datasets performing data preprocessing, feature engineering, and exploratory data analysis (EDA).
• Collaborate with cross-functional teams to integrate AI models into production-grade systems and services (e.g., via APIs, microservices).
• Optimize model performance and scalability through experimentation, tuning, and monitoring.
• Implement MLOps practices — CI/CD for ML, model versioning, automated retraining pipelines, and model monitoring.
• Research and evaluate emerging AI technologies, frameworks, and tools for potential adoption.
• Contribute to the development of internal AI tools, libraries, and best practices.

Required Skills & Qualifications

• Education: B.Tech/M.Tech/MS/Ph.D. in Computer Science, AI, Data Science, or related field.
• Experience: 4–8 years in AI/ML engineering, with hands-on deployment of models into production environments.
• Strong programming skills in Python (and familiarity with libraries such as TensorFlow, PyTorch, scikit-learn, etc.).
• Experience with data processing frameworks (Pandas, NumPy, Spark, etc.).
• Proficiency in ML lifecycle management tools and cloud platforms (AWS Sagemaker, Azure ML, GCP Vertex AI, MLflow, Kubeflow, etc.).
• Solid understanding of software engineering principles — version control (Git), CI/CD, containerization (Docker), and orchestration (Kubernetes).
• Good understanding of mathematics, statistics, and algorithms for AI/ML.
• Excellent problem-solving and communication skills.

Preferred / Nice-to-Have Skills

• Experience with LLMs (e.g., fine-tuning or deploying GPT, Llama, or other transformer-based models).
• Knowledge of Computer Vision (OpenCV, Detectron2) or NLP frameworks (Hugging Face, spaCy).
• Exposure to Vector Databases, Retrieval-Augmented Generation (RAG), or Generative AI applications.
• Familiarity with data pipelines (Airflow, Prefect) and monitoring tools (Prometheus, Grafana).
• Contributions to open-source ML/AI projects