Job responsibilities
- Executes software solutions, design, development, and technical troubleshooting with ability to think beyond routine or conventional approaches to build solutions or break down technical problems
- Creates secure and high-quality production code and maintains algorithms that run synchronously with appropriate systems
- Produces architecture and design artifacts for complex applications while being accountable for ensuring design constraints are met by software code development. Develop and implement AI/ML models and algorithms to solve business problems and integrate them into traditionally developed software products. Train and evaluate AI/ML models using large datasets.
- Gathers, analyzes, synthesizes, and develops visualizations and reporting from large, diverse data sets in service of continuous improvement of software applications and systems. Optimize and fine-tune AI/ML Design and develop data pipelines to preprocess and transform data for AI/ML models. models for performance and accuracy.
- Proactively identifies hidden problems and patterns in data and uses these insights to drive improvements to coding hygiene and system architecture
- Contributes to software engineering communities of practice and events that explore new and emerging technologies
- Adds to team culture of diversity, equity, inclusion, and respect
Required qualifications, capabilities, and skills
- Formal training or certification on software engineering concepts and 3+ years applied experience
- Demonstrated experience in applied AI/ML engineering.
- Programming skills in Python, with experience in developing and maintaining production-level code
- Proficiency in working with large datasets and data preprocessing.
- Solid understanding of AI/ML algorithms and techniques, including deep learning, time series forecasting and natural language processing.
- Experience with cloud platforms, such as AWS for deploying and scaling AI/ML models.
- Experience with ETL tools such as Airflow.
- Exposure to cloud technologies
Preferred qualifications, capabilities, and skills
- Experience in backend development, including databases (SQL/NoSQL/Graph), programming languages (Python/Java/Node.js), web frameworks, APIs, and Microservices. Knowledge of SRE practices
- Experience working with AWS EKS, ECS, RDS and DynamoDB. Exposure to cloud automation technologies such as Terraform
- Knowledge of large language models (LLMs) and accompanying toolsets the LLM ecosystem (e.g. Langchain, Vector databases, opensource Hugging Face Models)
- Ability to assist in assessing and choosing suitable LLM tools and models for diverse tasks including but not limited to curating custom datasets and fine-tuning LLM with a focus on parameter-efficient, mixture-of-expert, and instruction methods designing and developing advanced LLM prompts, Retrieval-Augmented Generation (RAG) solutions, and Intelligent agents for the LLMs and executing experiments to push the capability limits of LLM models and enhance their dependability.