Primary Responsibilities:
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Develop and implement advanced AI and machine learning strategies tailored for healthcare applications.
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Collaborate with cross-functional teams to identify, prioritize, and execute impactful AI/ML initiatives.
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Build, maintain, and optimize data pipelines for seamless data ingestion and model output management.
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Develop and execute scripts for ML model inference and performance evaluation.
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Design, implement, and manage CI/CD pipelines to support MLOps and DevOps functions.
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Provide expert guidance on tools, frameworks, and technologies to achieve desired business outcomes.
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Create and maintain comprehensive documentation for infrastructure design, deployment, and operational procedures.
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Utilize state-of-the-art AI/ML frameworks and tools to build robust, scalable solutions.
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Apply advanced techniques such as deep learning, natural language processing (NLP), computer vision, recommender systems, reinforcement learning, and large language models (LLMs).
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Communicate complex technical concepts and analytical results effectively to both technical and non-technical stakeholders.
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Adhere to company policies, procedures, and directives, including flexibility in work assignments, shifts, and team structures as required by the business.
Required Qualifications:
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Bachelors degree (or higher) in Computer Science, Engineering, or a related technical discipline.
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3+ years of experience in Software Engineering, AI/ML Engineering, or related domains.
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Proven experience in developing NLP-based solutions and managing relevant end-to-end projects.
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Hands-on expertise in building and deploying AI and ML solutions that drive innovation and efficiency.
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Strong experience with DevOps and CI/CD tools such as Git, Jenkins, Docker, and Kubernetes.
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Proficiency in working with and deploying Large Language Models (LLMs), including both open-source and OpenAI-based architectures.
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Familiarity with Retrieval-Augmented Generation (RAG) frameworks and design principles.
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Experience with UI and API development tools, including Streamlit, Flask, FastAPI, REST APIs, and containerized environments using Docker.
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Solid understanding of NLP tasks such as text classification, entity recognition, entity extraction, and question answering.
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Strong programming proficiency in Python and experience with Python libraries for data processing and model development.
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Demonstrated ability to design, develop, and deploy data pipelines and ML models on cloud platforms such as Azure, Databricks, and AzureML.
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Excellent analytical and problem-solving skills, with the ability to identify root causes and recommend effective solutions.