Job Description:
Technical Skills - Must Have:
Machine Learning And Analytics: End-to-end machine learning development lifecycle, including data preparation, data visualization, statistical analysis, feature engineering, predictive modeling, model deployment, and model monitoring. Strong experience with CI/CD, MLOps, and Generative AI solutions. Expertise in causal inference, time series analysis, forecasting, anomaly detection, hypothesis testing, and A/B testing. Experience with Git Actions, Tableau, Power BI, ThoughtSpot, and web scraping techniques. Data And Engineering:
Strong hands-on experience with SQL, MySQL, Postgres, Spark, S3, Trino, Data Factory, ETL processes, and data pipelines. Experience with Databricks and distributed computing frameworks. Programming Languages:
Proficiency in SQL, PySpark, Scala, R, Python, and SAS. Gen AI And Agent Frameworks:
Experience with prompt engineering, Retrieval-Augmented Generation (RAG), vector databases, and agentic frameworks. Hands-on experience with MCP, Large Language Models (LLMs), LangChain, LangGraph, HuggingFace, and explainable AI. Experience building conversational AI solutions, chatbots, LLM tuning, evaluation, and cost monitoring. Tools And Frameworks:
Experience using Git, TensorFlow, PyTorch, PySpark, AWS, MLflow, Docker, Kubernetes, Databricks, and SparkSQL. Hands-on experience with OpenCV, Azure, YOLO, Scikit-learn, FastAPI, Flask, Django, Keras, Pandas, NumPy, Polars, SciPy, Matplotlib, Seaborn, Plotly, and Streamlit. Cloud And MLOps:
Experience with AWS SageMaker, Azure ML, or GCP AI Platform. Strong hands-on expertise with Git, Docker, and CI/CD pipelines. Role Activities:
Design, develop, and deploy AI/ML and Generative AI models for enterprise and telecom use cases. Build, optimize, and maintain data pipelines for training, validation, and inference workflows. Develop scalable web-based AI applications using frameworks such as Flask, FastAPI, or Django. Implement LLM-based solutions including chatbots, summarization engines, and RAG-based architectures. Collaborate with data scientists, solution architects, and business stakeholders to translate functional requirements into technical solutions. Participate in proof-of-concept (PoC) development for AI/ML and automation initiatives. Conduct model evaluation, fine-tuning, and performance optimization. Integrate APIs, enterprise data sources, and cloud-based ML services across AWS, Azure, and GCP. Follow best practices in MLOps, model versioning, governance, and CI/CD integration. Prepare technical documentation, training materials, and demo presentations. Domain Skills Requirements:
Minimum 10+ years of experience in AI/ML development and Python-based solutions within Telecom or Retail domains. Desired Domain Experience:
Strong understanding of Telecom BSS and OSS domains, including fixed, mobile, IoT, and convergence services. In-depth knowledge of Business Support Systems (BSS) across sales, marketing, finance, product management, and customer care functions for CSPs. Experience with data integration for telecom B/OSS COTS platforms and data models such as Amdocs, NetCracker, and CSG. Preferred Qualifications:
Certification in AI/ML, Deep Learning, or Generative AI is a plus.
Machine Learning And Analytics: End-to-end machine learning development lifecycle, including data preparation, data visualization, statistical analysis, feature engineering, predictive modeling, model deployment, and model monitoring. Strong experience with CI/CD, MLOps, and Generative AI solutions. Expertise in causal inference, time series analysis, forecasting, anomaly detection, hypothesis testing, and A/B testing. Experience with Git Actions, Tableau, Power BI, ThoughtSpot, and web scraping techniques. Data And Engineering:
Strong hands-on experience with SQL, MySQL, Postgres, Spark, S3, Trino, Data Factory, ETL processes, and data pipelines. Experience with Databricks and distributed computing frameworks. Programming Languages:
Proficiency in SQL, PySpark, Scala, R, Python, and SAS. Gen AI And Agent Frameworks:
Experience with prompt engineering, Retrieval-Augmented Generation (RAG), vector databases, and agentic frameworks. Hands-on experience with MCP, Large Language Models (LLMs), LangChain, LangGraph, HuggingFace, and explainable AI. Experience building conversational AI solutions, chatbots, LLM tuning, evaluation, and cost monitoring. Tools And Frameworks:
Experience using Git, TensorFlow, PyTorch, PySpark, AWS, MLflow, Docker, Kubernetes, Databricks, and SparkSQL. Hands-on experience with OpenCV, Azure, YOLO, Scikit-learn, FastAPI, Flask, Django, Keras, Pandas, NumPy, Polars, SciPy, Matplotlib, Seaborn, Plotly, and Streamlit. Cloud And MLOps:
Experience with AWS SageMaker, Azure ML, or GCP AI Platform. Strong hands-on expertise with Git, Docker, and CI/CD pipelines. Role Activities:
Design, develop, and deploy AI/ML and Generative AI models for enterprise and telecom use cases. Build, optimize, and maintain data pipelines for training, validation, and inference workflows. Develop scalable web-based AI applications using frameworks such as Flask, FastAPI, or Django. Implement LLM-based solutions including chatbots, summarization engines, and RAG-based architectures. Collaborate with data scientists, solution architects, and business stakeholders to translate functional requirements into technical solutions. Participate in proof-of-concept (PoC) development for AI/ML and automation initiatives. Conduct model evaluation, fine-tuning, and performance optimization. Integrate APIs, enterprise data sources, and cloud-based ML services across AWS, Azure, and GCP. Follow best practices in MLOps, model versioning, governance, and CI/CD integration. Prepare technical documentation, training materials, and demo presentations. Domain Skills Requirements:
Minimum 10+ years of experience in AI/ML development and Python-based solutions within Telecom or Retail domains. Desired Domain Experience:
Strong understanding of Telecom BSS and OSS domains, including fixed, mobile, IoT, and convergence services. In-depth knowledge of Business Support Systems (BSS) across sales, marketing, finance, product management, and customer care functions for CSPs. Experience with data integration for telecom B/OSS COTS platforms and data models such as Amdocs, NetCracker, and CSG. Preferred Qualifications:
Certification in AI/ML, Deep Learning, or Generative AI is a plus.