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Architect end-to-end AI/ML solutions tailored to business requirements, ensuring scalability and performance.
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Experienced in use case discovery, scoping, and delivering complex solution architecture designs to multiple audiences requiring an ability to context switch in levels of technical depth across AI/ML technologies and platforms.
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Architect production level AI agent/RAG solutions for customers using our unified platform, including end-to-end ML pipelines, inference optimization, integration with cloud-native services and MLOps
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Provide guidance and best practices to development teams on AI agent implementations.
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Serve as the trusted technical advisor for customers developing GenAI solutions, such as RAG architectures on enterprise knowledge repo and ensure high-quality, efficient, and maintainable code for AI agents.
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Oversee the ingestion, transformation, and validation of large datasets for model training and deployment using AA or 3rd party hyper scaler platform.
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Implement and manage CI/CD pipelines for seamless model deployment and monitoring in production.
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Track and improve the performance of AI/ML systems through testing, validation, and iterative improvements.
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Stay updated on advancements in AI/ML and identify opportunities for integrating new techniques into solutions.
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Work closely with product managers, data scientists, and engineering teams to align on technical goals and deliverables.
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Create detailed architectural diagrams, technical documentation, and best practice guidelines.
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Address critical production issues and optimize system reliability and performance.
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Mentor team members and contribute to organizational learning in AI/ML technologies.
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8-10 yrs of overall IT experience with 5+ years of experience in AI/ML implementations
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Deep knowledge of machine learning, deep learning, and natural language processing algorithms and frameworks (TensorFlow, PyTorch, Hugging Face, etc.).
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Experience with the latest techniques in natural language processing including vector databases, fine-tuning LLMs, and deploying LLMs with tools such as HuggingFace, Langchain, and OpenAI
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Hands-on experience with AI/ML services on AWS, GCP, or Microsoft Azure. Have completed certifications from either of these hyper scaler providers.
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Strong programming skills in Python, Java, or other relevant languages;
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Familiarity with RAG (Retrieval-Augmented Generation) methodologies and integrating Generative AI into enterprise applications.
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Hands-on expertise in prompt engineering, RAG, vector DBs and technologies to implement AI agent solutions.
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Expertise in data preprocessing, feature engineering, and working with large-scale data pipelines (Apache Spark, Kafka, etc.).
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Strong background on cloud services from various cloud service providers that integrates with AI/ML solutions
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Experience with MLOps tools like MLflow, Kubeflow, or SageMaker.
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Proficiency in deploying, monitoring, and optimizing models in production environments.
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Ability to design scalable, secure, and high-performance AI/ML solutions.
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Strong analytical skills to address complex technical challenges and innovate scalable solutions.
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Ability to articulate technical concepts to diverse audiences, including non-technical stakeholders.
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Experience mentoring engineers and collaborating with cross-functional teams.
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Understanding of data privacy and AI ethics for compliant system designs.
