CT_NITRO Experimental AI Engineer

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• 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

• 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.

• 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 of cloud ecosystems;
  Azure is preferred.
• Experience with machine‑learning frameworks and tools (e.g., sci‑kit‑learn, Keras, Tensor Flow, 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.
• Visualization and storytelling – Capability to assimilate and present data, as well as more advanced visualization techniques, and 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 communication skills.
• Education:
  • Bachelor/Master/PG in Artificial Intelligence, Analytics, Data Science, Computer Science, Engineering, Information Technology, Social Sciences or related fields preferred.

• Strong analytical skills and problem‑solving ability.
• A self‑starter, independent‑thinker, curious and creative person with ambition and passion.
• Excellent inter‑personal, communication, collaboration, and presentation skills.
• Customer focused.
• Excellent time management skills.
• Positive and constructive mindset.
• Takes responsibility for continuous self‑learning.
• Takes the lead and makes decisions in critical times and tough circumstances.
• A…