AI Engineer - Software
Indexed description
Responsibilities Include
Machine Learning & Model Development
- Design, train, validate, and deploy machine learning models using modern frameworks and best practices.
- Build end ‑ to ‑ end ML pipelines that include data ingestion, preprocessing, feature engineering, training, evaluation, and monitoring.
- Optimize models for performance, scalability, and efficiency.
- Collect, clean, transform, and structure complex datasets from diverse sources.
- Perform exploratory data analysis to identify trends, anomalies, and opportunities.
- Develop automation for data processing workflows and ensure high data quality.
- Create intelligent agents capable of autonomous decision ‑ making, workflow automation, and contextual reasoning.
- Integrate agents with internal systems, APIs, and knowledge bases.
- Evaluate agent performance and iterate based on measurable outcomes.
- Stay current with the rapidly evolving AI/ML landscape, including new algorithms, architectures, tools, and best practices.
- Prototype innovative AI solutions using cutting ‑ edge techniques such as LLMs, RAG pipelines, multi-agent systems, and generative models.
- Develop technical briefs, proofs of concept, and recommendations for adopting new technologies.
- Work closely with software engineers, data scientists, product teams, and stakeholders to translate business needs into AI solutions.
- Document technical designs, research findings, and model performance.
- Provide guidance and mentorship on AI/ML concepts and toolsets.
- Bachelor's of Science degree or Master’s degree in Computer Science, Data Science, or AI Engineering
- 5+ years of post-graduate related experience in developing software applications
- Strong proficiency in Python and familiarity with ML libraries such as TensorFlow, PyTorch, scikit-learn, or similar.
- Strong proficiency with application APIs, web services, and data management approaches in applications
- Experience with data wrangling tools and technologies (Pandas, SQL, ETL systems).
- Solid understanding of machine learning algorithms, statistical modeling, and model evaluation.
- Experience working with LLMs or generative AI models.
- Knowledge of cloud platforms (Azure, AWS, GCP) and containerization technologies.
- Experience building AI agents or autonomous systems.
- Familiarity with vector databases, RAG architectures, or multi‑modal models.
- Exposure to MLOps practices (CI/CD, model monitoring, feature stores).
- Contributions to AI research, open-source tools, or AI‑related publications.
- Knowledge of distributed computing or GPU optimization.
- Excellent verbal and written communication skills
- Strong organizational and interpersonal skills
- Ability to multi-task in a fast-paced environment
- Ability to work on a cross-functional team as well as independently
- Curiosity, adaptability, and enthusiasm for continuous learning.
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