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.