Job Title: Senior Data Scientist / Agentic AI-ML Engineer
Location: Dallas (Onsite)
Duration: Long-Term
Experience: 10+ Years
Rate: $52 per hour
Job Description
We are seeking a Senior Data Scientist / Agentic AI-ML Engineer with 11+ years of experience in building enterprise AI, Machine Learning, and Generative AI solutions. The ideal candidate should have strong expertise in LLMs, Agentic AI, RAG, LangChain/LangGraph, Python, FastAPI, Azure OpenAI, AWS, and cloud-native AI architectures.
Required Skills
11+ years in AI/ML, Data Science, and Predictive Analytics
Strong Python, SQL, Scikit-learn, TensorFlow, PyTorch
Hands-on with GPT-4, Claude, Gemini, Llama, LangChain, LangGraph, CrewAI, AWS Bedrock
Experience building RAG applications, vector databases (Pinecone, FAISS, ChromaDB, pgvector), and semantic search
NLP expertise with Hugging Face, spaCy, NLTK, embeddings, prompt engineering
Cloud experience with AWS, Azure, and Google Cloud Platform
MLOps using Docker, Kubernetes, MLflow, Kubeflow, CI/CD
Big Data technologies including Spark, Kafka, Databricks
Experience with FastAPI, REST APIs, AI solution architecture, and enterprise AI platforms
Preferred domain experience in Telecom, Banking, Healthcare, Retail, or E-Commerce
Mandatory Submission Requirements
Senior Data Scientist / Agentic AI-ML Engineer with 11+ years of experience delivering end-to-end AI, Machine Learning,
and Generative AI solutions across Telecom, Banking, Healthcare, E-commerce, and Retail domains.
· Currently contributing to Next-Gen AI initiatives within the Veloce platform at T-Mobile, exploring and prototyping
AI/ML and Generative AI capabilities to enhance automation, knowledge retrieval, and decision support across telecom
workflows.
· Extensive expertise in Python, Azure OpenAI, AWS, Large Language Models (LLMs), Retrieval-Augmented Generation
(RAG), LangChain, LangGraph, FastAPI, Predictive Analytics, Intelligent Automation, AI Solution Design, and
Enterprise AI Platforms, delivering scalable cloud-native AI solutions.
· Hands-on experience developing Machine Learning models such as BOM ancillary prediction systems, leveraging
historical telecom deployment data to automate component recommendations and improve operational efficiency.
· Designed and implemented Generative AI solutions including Retrieval-Augmented Generation (RAG) chatbots to
retrieve enterprise knowledge from Confluence documentation using LLMs, embeddings, and vector search.
· Strong expertise in LLM integration and GenAI frameworks including LangChain, LangGraph, CrewAI, and AWS Bedrock,
enabling conversational AI assistants and intelligent enterprise knowledge systems.
· Skilled in RAG architecture design, vector embedding pipelines, and semantic search systems using vector databases
such as ChromaDB, pgvector, FAISS, and Pinecone.
· Proficient in Python-based AI stack including Pandas, NumPy, Scikit-learn, FastAPI, and modern AI frameworks for building
scalable AI services and backend APIs.
· Experience designing cloud-native AI architectures and MLOps pipelines using AWS, Azure, and Google Cloud Platform, integrating CI/CD,
containerization, and infrastructure automation.
· Strong background in NLP and text analytics, including named entity recognition, sentiment analysis, topic modeling,
summarization, embeddings, and enterprise knowledge retrieval systems.
· Demonstrated leadership in cross-functional collaboration, AI solution architecture, and translating complex data
insights into scalable enterprise AI systems
TECHNICAL SKILLS:
Category Technologies / Tools
Programming Languages Python, R, SQL, JavaScript
ML / DL Frameworks Scikit-learn, TensorFlow, PyTorch, Keras, XGBoost, LightGBM, CatBoost, FastText
Generative AI / LLMs GPT-4, Gemini, Claude, LLaMA (2/3), Falcon, Mistral, BioBERT, ClinicalBERT, T5, BERT, LangChain,
LangGraph
NLP Tools spaCy, NLTK, Gensim, TextBlob, Hugging Face Transformers, Prompt Engineering, RAG Pipelines
Graph & Multimodal AI Neo4j, NetworkX, DGL, PyG, GNNs, Vision Transformers (ViT), ResNet, EfficientNet, CLIP
MLOps & Automation MLflow, Kubeflow, Airflow, Jenkins, GitLab CI/CD, Docker, Kubernetes, Terraform, Vertex AI, AWS
SageMaker, Azure ML
Cloud Platforms AWS (S3, EC2, SageMaker, Lambda, Glue, Redshift), Google Cloud Platform (Vertex AI, BigQuery, Dataflow), Azure (ML,
Databricks, Cognitive Services)
Vector Databases & Search FAISS, Pinecone, Weaviate, ElasticSearch, ChromaDB
Big Data Ecosystem Apache Spark, Kafka, Hadoop, Hive, HBase, Cassandra, Databricks
Data Visualization Tableau, Power BI, R Shiny, Plotly, Dash, Seaborn, Matplotlib
Databases PostgreSQL, MySQL, Oracle, MongoDB, Cassandra
Machine Learning Techniques Regression, Classification, Clustering (K-Means, DBSCAN), Reinforcement Learning (DQN, PPO),
Anomaly Detection, Time Series (ARIMA, Prophet)
Deep Learning Models CNN, RNN, LSTM, GANs, Autoencoders, Siamese Networks, Transformers
Security & Compliance HIPAA, GDPR, PHI/PII Masking, IAM, OAuth 2.0, API Gateway, Responsible AI, SHAP, LIME
Development Tools Jupyter Notebook, VSCode, PyCharm, Git, Apache NiFi, Jenkins
Monitoring / Observability Prometheus, Grafana, CloudWatch, SageMaker Model Monitor
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