Need::AI Engineer at Atlanta, GA or Dallas, TX (day1 onsite/hybrid) Contract Position

Role: AI Engineer
Location : Atlanta, GA or Dallas, TX (day1 onsite/hybrid)

Rate: $50/hr CTC All Inclusive

Client HCL

Job Description (Posting). : We are looking for a talented AI Engineer with 3+ years of experience to work on the development, optimization, and monitoring of our OpsGPT and Avertack GPT systems. In this role, you will be responsible for implementing AI-driven solutions to automate, optimize, and enhance operational efficiency within a telecom-focused environment. Your work will include prompt engineering, visualization, end-to-end monitoring, ontology research, and LLM tuning to ensure the best performance for our AI-based applications.
Key Responsibilities

1. Functional OpsGPT Chatbot Development:
   1. Design and develop a functional chatbot with foundational capabilities to handle telecom-related operational queries.
   2. Define and implement conversation flows, response templates, and basic troubleshooting actions.
   3. Collaborate with data teams to integrate relevant data sources and ensure contextually accurate responses.
2. Prompt Engineering Optimization:
   1. Experiment with various prompt structures to improve response accuracy and task efficiency.
   2. Conduct A/B testing on prompt formats and optimize based on user feedback.
   3. Document and maintain prompt design guidelines for consistent optimization practices.
3. Use Case Design for Avertack GPT:
   1. Identify and design specific use cases for Avertack GPT within telecom operations (e.g., predictive maintenance, network optimization).
   2. Work with stakeholders to translate business needs into actionable AI solutions and workflows.
   3. Prototype workflows for each use case, ensuring alignment with business objectives.
4. Visualization and Dashboarding:
   1. Build visualizations using charts and Kibana dashboards to track key metrics such as chatbot interaction volume, response times, and success rates.
   2. Enable filtering, segmentation, and drill-down features to allow detailed analysis of system performance.
   3. Collaborate with cross-functional teams to make real-time metrics accessible and actionable.
5. End-to-End Monitoring of Transactions:
   1. Implement comprehensive monitoring for all Avertack GPT transactions, capturing metadata at each stage (e.g., request received, response generated).
   2. Set up alerting for anomalies or threshold breaches, such as increased response time or error rates.
6. Ontology Research and Graph DB Integration:
   1. Research ontology applications and define core entities and relationships relevant to telecom operations.
7. Tuning and Auto-Integration of Large Language Models (LLM):
   1. Fine-tune the LLM on domain-specific telecom data to improve accuracy and relevance.

Required Skills & Qualifications

• Bachelor’s or Master’s degree in Computer Science, Artificial Intelligence, Data Science, or a related field.
• 3–5 years in AI engineering, machine learning, or a similar role, preferably in a telecom or operationally focused environment.
• Technical Skills:
  • Proficiency in Python for AI and ML development.
  • Strong experience with ML frameworks such as TensorFlow, PyTorch, and Scikit-Learn.
  • Practical experience with NLP tools and prompt optimization techniques.
  • Experience with visualization tools (e.g., Kibana, Power BI) and charting libraries (e.g., Matplotlib, Seaborn).
  • Familiarity with big data platforms (e.g., Apache Spark) and Graph DBs (e.g., Neo4j) for ontology integration.
  • Proficiency in using monitoring tools to track performance metrics and troubleshoot issues in real time.
• Strong problem-solving and analytical skills to drive data-driven improvements in AI models and operational workflows.
• Ability to collaborate effectively with technical and non-technical stakeholders, clearly explaining complex AI concepts.

Preferred Qualifications

• Understanding of telecom-specific data formats, metrics, and industry protocols.
• Experience with optimization algorithms (e.g., reinforcement learning) for network performance enhancement.

Relevant certifications, such as AWS Certified Machine Learning, Google Professional Machine Learning Engineer, or telecom-related certifications.