Artificial Intelligence Engineer

About the Role

AI Engineer

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:

  48 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.

  Role & responsibilities