Posted:1 month ago|
Platform:
On-site
Full Time
∙Lead the design, development, and deployment of Generative AI models and solutions on cloud
platforms such as Microsoft Azure, AWS, or GCP.
∙Collaborate with cross-functional teams to understand business requirements and translate them
into technical solutions.
∙Drive the end-to-end lifecycle of AI/ML projects, from data collection and preprocessing to
model training, evaluation, and deployment.
∙Mentor and guide junior team members, fostering a culture of continuous learning and
innovation.
∙Stay updated with the latest advancements in Generative AI and AI/ML technologies, and
integrate them into ongoing projects.
∙Optimize and fine-tune AI models for performance, scalability, and reliability.
∙Develop and maintain comprehensive documentation for all AI/ML projects and processes.
∙10-13 years overall IT experience. Out of which 3-4 years hands-on experience in AI/ML, and at
least 1 year in Generative AI.
∙Strong proficiency in Python programming and experience with AI/ML frameworks, models and
algorithms.
∙Strong experience with leading LLMs, RAG, agentic automation, and agent frameworks like
Lang Chain, Lang Graph, AutoGen, AutoGPT for Generative AI use-cases.
∙Hands-on experience in Gen AI observability & monitoring via tools like Lang Smith
∙Experience with deep learning, CNN, NLP, text-to-speech, speech-to-text, OCR, and image
processing techniques.
∙Experience in Infrastructure related use-cases leveraging Autonomous Agents and/or agentic
platform. Should have hands-on experience building Autonomous agents for Gen AI related use-
cases.
∙Extensive experience with cloud platforms (Microsoft Azure/AWS/GCP/IBM WatsonX) and their
AI/ML services.
∙Extensive experience in Prompt Engineering and ability to design and build Prompt libraries for
various scenarios applicable in Infrastructure.
∙Thorough understanding of Responsible AI best practices and standards and experience of
implementing them in Gen AI related projects
∙Proven track record of leading and delivering complex AI/ML projects.
∙Excellent problem-solving skills and the ability to work in a fast-paced, collaborative
environment.
∙Strong communication and leadership skills, with the ability to articulate technical concepts to
non-technical stakeholders.
∙Experience with natural language processing (NLP) and computer vision.
∙Familiarity with MLOps practices and tools.
∙Knowledge of data engineering and big data technologies.
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