Job Description
Skills:
Data Science, Deep Learning, Machine Learning, Computer vision, CNN, YOLO, AI/ML, GenAI,
Key Responsibilities
Design, build, and deploy production-grade AI, ML, and GenAI solutions at enterprise scale.
Architect and implement LLM-powered applications using GPT, Mistral, LLaMA, Gemini, and similar models.
Build Retrieval-Augmented Generation (RAG) pipelines using vector databases (Azure AI Search).
Develop multi-agent AI systems using LangGraph (orchestrator, intent, guard, domain agents).
Implement robust prompt engineering, hierarchical prompting, and LLM guardrails.
Develop ML Models For
Forecasting & time-series prediction
Recommendation & personalization systems
Fraud & anomaly detection
Churn and user behavior analysis
Sentiment analysis and NLP-driven insights
Build NLP and Deep Learning solutions using transformer models, embeddings, CNNs, RNNs, and LSTMs.
Manage the end-to-end ML lifecycle including experimentation, training, deployment, and monitoring.
Deploy models using Azure Managed Online Endpoints and ensure scalability and observability.
Collaborate with business stakeholders to translate problems into AI-driven solutions.
Mentor junior data scientists and contribute to AI strategy and technical roadmaps.
Required Skills & Experience
5+ years of hands-on experience in Data Science, Machine Learning, Deep Learning, NLP, and GenAI.
Strong programming expertise in Python and SQL (R is a plus).
Hands-on Experience With
ML frameworks: scikit-learn, XGBoost, CatBoost
Deep Learning: TensorFlow, PyTorch, Keras, FastAI
Strong experience with LLMs, RAG pipelines, and multi-agent AI systems.
Proven experience in NLP techniques including BERT, embeddings, topic modelling, and sentiment analysis.
Experience with MLOps tools such as MLflow, CI/CD pipelines, and model registries.
Hands-on cloud experience with Microsoft Azure (GCP/AWS exposure is a plus).
Experience handling large datasets using SQL, PySpark, and cloud-native data services.
Strong understanding of model evaluation, statistical testing, and A/B testing.
What We Look For
Strong problem-solving and analytical mindset.
Hands-on experience delivering production-grade AI and GenAI systems.
Ability to bridge cutting-edge GenAI research with real-world enterprise use cases.
Experience building scalable, reliable, and secure AI platforms.
Strong communication skills to collaborate with product managers, engineers, and business teams.
Ability to mentor team members and lead technical discussions and design decisions.
Comfortable working in consulting, product-driven, or fast-paced environments.
