CT_NITRO Experimental AI Engineer

The selected candidate

• Leads the design, development, and deployment of advanced generative AI models, including large language models (LLMs), driving innovation and solving complex business challenges
• Oversees the end-to-end data science lifecycle, from data acquisition and preprocessing to model validation and optimization, ensuring best practices and high-quality outcomes
• Works on developing and deploying machine learning algorithms, ensuring models are optimized for performance and scalability
• Mentors and guides junior data scientists, fostering a collaborative environment and promoting knowledge sharing of cutting-edge techniques and tools
• Partners with cross-functional teams to integrate AI-driven insights into products and services, translating sophisticated models into practical, scalable solutions that drive measurable impact

Your key responsibilities include

• Implementing AI solutions that integrate seamlessly with existing business systems to enhance functionality and user interaction.
• Reviewing complex data sets to establish data quality and highlighting where data cleansing is required to remediate the data
• Designing and implementing data models to manage data within the organization,
• Migrating data from one system to another using multiple sources, identifying and implementing storage requirements
• Analyzing the latest trends such as cloud computing and distributed processing, and their uses in business, building industry knowledge
• Peer reviewing models and working with relevant business areas to seek their input and ensuring they are fit for the designed purpose
• Automating important infrastructure for the data science team.
• Staying current with AI trends and suggesting improvements to existing systems and workflows.

Skills and attributes for success

• 8+ years’ experience in machine learning, large scale data acquisition, transformation, and cleaning, both structured and unstructured data
• Experience with generative LLM fine-tuning and prompt engineering.
• Proficiency with data wrangling, visualization, and modeling in Python
• Experience in ML/NLP algorithms, including supervised and unsupervised learning.
• Good knowledge with cloud ecosystems, Azure is preferred   
• Experience in machine learning frameworks and tools (e.g., sci-kit-learn, Keras, TensorFlow, MXNet, PyTorch, MLlib)
• Strong programming skills in Python 
• Experience with tools in the distributed computing, GPU, cloud platforms, and Big Data domains (e.g., GCP, AWS, MS Azure, Hadoop, Spark, Databricks) is a plus
• Experience with SQL, Document DBs and Vector DBs
• Batch Processing - Capability to design an efficient way of processing high volumes of data where a group of transactions is collected over a period Data 
• Visualization and storytelling - Capability to assimilate and present data, as well as more advanced visualization techniques. Capability to engage and explain data creating a narrative around data visualization​
• Knowledge of containerization platforms like Docker is a plus.
• Experience with Git and modern software development workflow.
• Non-Technical Skills:
  
  • Strong team and business communication skills – can walk through and approach with technical clients or explain tricky concepts to non-technical people
  • Must thrive in a fast-paced environment and be able to work independently. 
  • Excellent in communication skills
• Education
  
  • Bachelor/Master/PG in Artificial Intelligence, Analytics, Data Science, Computer Science, Engineering, Information Technology, Social Sciences or related fields preferred

What we look for

• Strong analytical skills and problem-solving ability
• A self-starter, independent-thinker, curious and creative person with ambition and passion
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