Job title: Software Developer
Job Type: Full-time
Job Location: Lake Mary FL 32746 (Hybrid)
DOE Salary & Benefits Offered
Requirements:
- Design and execute conversational sequences within the Dialogflow platform, encompassing the development of conversational responses at the API level and their seamless integration into Dialogflow conversation flows.
- Work on voice and text-based chatbots, offering expertise and recommendations in areas such as healthcare, prescription refills, mental health, and clinical assistance.
- Leverage Google Dialogflow to integrate chatbots and IVRs seamlessly across multiple platforms, including Twilio, Telegram, WhatsApp, Slack, and various media platforms, enhancing the overall user support experience.
- Enhance user engagement by generating content for Frequently Asked Questions (FAQs) covering topics such as drugs, medication, orders, payments, and other essential information. This content will be seamlessly integrated into relevant web pages.
- Implement a robust Question & Answering solution engine for natural language database queries using LangChain technology. Additionally, integrate ChatGPT Rest API with Apple Siri for enhanced voice-based interactions and user assistance.
- Design and build Enterprise Search applications, Contextual Chatbots, and Recommendation Systems using Python, Data Science, Machine Learning, and NLP technologies.
- Implement voice and speech-to-text algorithms using OpenAI for multiple languages and perform question and social media analysis for product marketing.
- Create machine learning models for real-time document translation into multiple languages and employ various text processing and feature extraction techniques.
- Implement Learning to Rank algorithms for refined document ranking decisions using trained machine learning models.
- Investigate intent detection and named entity recognition methods for chatbots and Develop machine learning and deep learning algorithms and pipelines for text analytics.
- Conduct exploratory data analysis (EDA) using Pandas, Dask, and Scikit-Learn to extract features and build statistical models based on ML algorithms.
- Deploy machine learning models on GCP, Azure, AWS, and other cloud platforms using MLOPS techniques such as PyCaret, PyMLPipe, and monitor model performance with Streamlit.
- Collaborate in the development of AI language models, including prompt engineering to fine-tune model responses and behavior.
- Developing High-Quality Prompts: Create and refine prompts that are clear, effective, and contextually relevant to guide AI language models in producing desired responses.
- Model Training Guidance: Collaborate with machine learning engineers and data scientists to provide input and guidance during the training and fine-tuning of AI models, ensuring prompts align with the intended model behavior.
- Data Annotation: Assist in the annotation of training data, including prompt-response pairs, to improve model performance. Ensure that annotations are consistent and accurately represent the desired output.
- Continuous Iteration: Continuously review and analyze model outputs to identify areas for prompt refinement and improvement. Iteratively adjust prompts to address any model biases, errors, or inconsistencies.
- Performance Monitoring: Implement monitoring mechanisms to track the performance of AI models in response to different prompts. Analyze data to identify trends, issues, or opportunities for prompt optimization.
- Naturalness and Coherence: Ensure that prompts generate responses that are natural and coherent, aligning with the desired conversational flow and user experience.
- User Feedback Integration: Collect and integrate user feedback to refine prompts and improve model responsiveness to user queries and interactions.
- Ethical Considerations: Maintain awareness of ethical considerations and potential biases in prompt engineering, taking steps to mitigate biases and ensure responsible AI development.
- Design and implement solutions for migrating or building applications on public and private clouds, with a focus on creating petabyte-scale data lakes ETC.
Skills & Certifications:
- Bachelor’s Degree in or any Science or Computer Systems, Information Technology, Information Systems, Information Technology, Computer Engineering, Computer Applications etc.
- Data Engineering, Natural Language Processing (NLP), Deep Learning, Prompt Engineering
- Google Dialog flow, RASA Chatbot
- Recommender Systems, Master Computer VisionTM OpenCV4 in Python with Deep Learning.
- AZ Machine Learning, Azure Databricks,
- Python ML & AI, Azure Data bricks, Spark, Kafka, Statistics Essential, Big Data Hadoop and Spark.
- Machine Learning, Deep Learning, Prompt Engineering, AI, Python, Data Engineering, Data Lakes, Data architecture, Data analysis/Analytics.