Gen AI Lead

Job Description

• The Gen AI Lead will design, develop, and deploy advanced AI, Machine Learning, and Generative AI solutions for enterprise and telecom use cases.
• This role involves building scalable data pipelines, implementing LLM-based applications, leading AI proof-of-concepts, and ensuring best practices in MLOps, CI/CD, and model lifecycle management while collaborating closely with cross‑functional teams.

• 10+ years of experience in AI/ML development with strong Python‑based solutions.
• Strong experience across the complete machine learning development lifecycle.
• Hands‑on experience with Generative AI, LLMs, and agent‑based frameworks.
• Experience with cloud platforms and MLOps practices.

• AI/ML development in Telecom or Retail domains.
• End‑to‑end experience in model development, deployment, and monitoring.
• Experience working with large‑scale data pipelines and distributed computing.

• Design, develop, and deploy AI/ML and Generative AI models for enterprise and telecom use cases.
• Build and optimize data pipelines for training, validation, and inference workflows.
• Develop web‑based AI applications using frameworks such as Flask, FastAPI, or Django.
• Implement LLM‑based solutions including chatbots, summarization, and RAG‑based systems.
• Collaborate with data scientists, solution architects, and business teams to translate functional requirements into technical solutions.
• Participate in proof‑of‑concept development for AI, ML, and automation initiatives.
• Perform model evaluation, fine‑tuning, and performance optimization.
• Work with APIs, diverse data sources, and cloud‑based ML services.
• Apply best practices in MLOps, CI/CD integration, and model versioning.
• Prepare technical documentation, training materials, and demonstration presentations.

• Machine Learning lifecycle, MLOps, CI/CD, and Generative AI.
• Statistical analysis, feature engineering, forecasting, anomaly detection, and hypothesis testing.
• Prompt engineering, RAG, vector databases, agentic frameworks, and LLM evaluation.
• Programming languages including Python, SQL, PySpark, Scala, R, and SAS.
• Data engineering tools such as SQL, Spark, Databricks, ETL, and distributed computing.
• Frameworks and tools including Tensor Flow, PyTorch, MLflow, Docker, Kubernetes, and Git.
• Cloud platforms and ML services across AWS, Azure, or GCP.
• Data visualization and analytics tools such as Tableau, Power BI, and similar platforms.

• Bachelor’s degree in Computer Science, Engineering, or a related field.
• Certification in AI, Machine Learning, Deep Learning, or Generative AI is preferred.