Responsibilities

• A keen desire to solve business problems, and to find patterns and insights within structured and unstructured data.
• Ability and willingness to work independently and lead a team.
• Excellent written and verbal communication skills for coordinating across teams.
• A drive to learn and master new technologies and techniques.
• Strong ability to formulate business problems mathematically and strong problem-solving skills.
• Proven data manipulation skills.
• Have been active in the community in terms of articles / blogs / speaking engagements at conferences.
• Lead the design, development, and deployment of Generative AI models (e.g., GANs, VAEs) for various business applications.
• Play a key role in establishing and implementing MLOps practices within the team, ensuring efficient and scalable machine learning workflows, including deployment on cloud infrastructure like AWS, GCP, Vertex AI, and Azure.
• Work closely with business stakeholders to understand their needs and translate them into actionable data science solutions leveraging Deep Learning and Machine Learning techniques.
• Stay up-to-date on the latest advancements in Generative AI, MLOps, Deep Learning, Machine Learning, and other relevant data science techniques.
• Effectively communicate complex technical concepts to both technical and non-technical audiences.
• Collaborate with cross-functional teams (engineering, product, etc.) to ensure successful integration of data science solutions.

Requirements

• At least 6+ years of experience with recent 6 years associated in Data science.
• Proven experience in building and deploying Generative AI(e.g., GANs, VAEs, LLMs, RAG, LoRA/QLoRA, ChatAPIs, CSV Agents, Vector DBS, Langchain) for real-world applications.
• Experience of MLOPs over cloud (Azure/AWS/GCP) and CICD Pipeline.
• Working experience on containerizing ML models.
• Proficiency in Python programming with strong experience in deep learning frameworks like PyTorch or TensorFlow.
• Solid understanding of core Machine Learning concepts (supervised, unsupervised, reinforcement learning) and algorithms (linear regression, decision trees, random forests, etc.).
• Experience with data manipulation libraries (e.g., Pandas) and data visualization tools (e.g., Matplotlib).
• Working knowledge of Databricks for large-scale data processing and model training.
• Solid knowledge of Natural Language Processing (NLP) using word embeddings, BERT, Gensim, Fasttext etc.
• Solid experience with data visualization tools such as Matplotlib/ggplot/plotly
• Significant experience in implementation of Deep Learning based solutions in NLP and/or Computer vision/ Image processing.
• Hands-on Experience with deep learning frameworks such as PyTorch, TensorFlow , MxNet etc.
• Strong problem solving skills with an emphasis on domain use-case.
• Excellent communication and collaboration skills to lead and mentor a team, as well as interact effectively with stakeholders.