Artificial Intelligence Engineer

Key Responsibilities

1. Model Development & Optimization

• Build, train, and fine-tune machine learning and deep learning models.
• Implement NLP, computer vision, or recommendation systems depending on project needs.
• Conduct feature engineering, data preprocessing, and experiment design.
• Optimize model performance through hyperparameter tuning and algorithm improvements.

2. Data Engineering & Pipelines

• Design and maintain scalable data pipelines for model training and inference.
• Work with ETL processes, data warehousing, and big data frameworks (e.g., Spark, Kafka).
• Ensure data quality, governance, and security.

3. AI Deployment & MLOps

• Deploy models to production using cloud services (AWS/GCP/Azure).
• Build CI/CD pipelines for ML workflows.
• Monitor model performance and drift; manage retraining pipelines.

4. Software Engineering

• Develop robust, production-grade code in Python, Java, or similar languages.
• Implement APIs and microservices for model inference.
• Collaborate closely with backend, frontend, and DevOps teams.

5. Research & Innovation

• Evaluate emerging AI technologies, models, and frameworks.
• Experiment with LLMs, generative AI, and new architectures.
• Translate research prototypes into production-ready solutions.

Required Skills

Technical Skills

• Proficiency in ML libraries and frameworks (TensorFlow, PyTorch, Scikit-learn).
• Strong programming skills in Python (preferred), C++, or Java.
• Experience with cloud platforms (AWS Sagemaker, GCP Vertex AI, Azure ML).
• Familiarity with vector databases, embeddings, RAG pipelines, or LLM orchestration.
• Knowledge of data structures, algorithms, and system design.
• Understanding of MLOps tools (Kubeflow, MLFlow, Docker, Kubernetes).

Soft Skills

• Strong analytical and problem-solving abilities.
• Ability to communicate complex technical concepts clearly.
• Collaboration with cross-functional teams.
• Adaptability in fast-paced environments.

Education & Experience

• Bachelor’s or Master’s degree in Computer Science, AI, Data Science, Engineering, or related field.
• 2–5 years of experience with machine learning/AI development (for mid-level roles).
• Experience working with large datasets and high-scale systems.

Preferred (Good to Have)

• Experience with generative AI (GPT, diffusion models, fine-tuning, prompt engineering).
• Familiarity with LLM frameworks like LangChain, LlamaIndex, Haystack.
• Understanding of model compression, quantization, distillation.
• Experience with multi-modal AI or reinforcement learning.