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.