TechRBM

SAS Nagar Mohali

Role Overview Were hiring a Senior Data Scientist who can own end-to-end problem solving-from business discovery and hypothesis design to model deployment and post-production monitoring. You will partner with product, engineering, and client stakeholders to build production-grade ML/AI and GenAI solutions on AWS/Azure/GCP and mentor a small pod (2-5) of data scientists/ML engineers. Key Responsibilities Business & Problem Framing: Engage with client stakeholders to translate objectives into measurable DS/ML use cases, define success metrics (ROI, adoption, accuracy, latency), and create experiment plans. Data Strategy & Feature Engineering: Own data acquisition, quality checks, EDA, and feature pipelines across SQL/Spark/Databricks; collaborate with Data Engineering for robust ingestion and transformation (Airflow/dbt). Modeling: Build, tune, and compare models for supervised/unsupervised learning, time-series forecasting, NLP/CV, and GenAI (RAG, fine-tuning, prompt-engineering) using Python (pandas, NumPy, scikit-learn, XGBoost/LightGBM), PyTorch/TensorFlow, Hugging Face. MLOps & Deployment: Productionize via MLflow/DVC, model registry, CI/CD (GitHub/GitLab), containers (Docker/Kubernetes), and cloud ML platforms (SageMaker/Azure ML/Vertex AI). Expose services via FastAPI/Flask; implement monitoring for drift, data quality, and model performance. Experimentation & Causality: Design and analyze A/B tests, apply causal inference techniques (e.g., propensity scoring, DiD) to measure true impact. Explain ability, Fairness & Compliance: Apply model cards, SHAP/LIME, bias checks, PII handling, anonymization/pseudonymization, and align with applicable data privacy regulations (e.g., GDPR/DPDP). Visualization & Storytelling: Build insights dashboards (Tableau/Power BI/Plotly) and communicate recommendations to senior business and technical stakeholders. Collaboration & Leadership: Mentor juniors, conduct code and research reviews, contribute to standards, and support solutioning during pre-sales/POCs. Required Skills & Experience Experience: 7-10 years overall, with 5+ years in applied ML/Data Science delivering models to production for enterprise clients. Programming & Data: Expert Python, advanced SQL, and hands-on with Spark/Databricks. Strong software practices (testing, typing, packaging). ML/AI Stack: scikit-learn, XGBoost/LightGBM; PyTorch or TensorFlow; NLP (spaCy, Transformers, embeddings), vector DBs (FAISS/Pinecone), LangChain/LlamaIndex for RAG. Cloud & MLOps: Real-world deployments on AWS/Azure/GCP using SageMaker/Azure ML/Vertex AI; MLflow, model registry, feature store, Docker/K8s, and CI/CD. Experimentation & Analytics: A/B testing, Bayesian/ frequentist methods, causal inference, statistical rigor. Visualization & Communication: Storytelling with data; Tableau/Power BI/Plotly, executive-ready presentations. Domain Exposure (nice-to-have): BFSI risk/collections/CLV, retail demand/personalization, healthcare claims/clinical NLP, manufacturing quality/predictive maintenance. Bonus: Recommenders, time-series, graph ML, optimization (OR), reinforcement learning, geospatial analytics. Education & Certifications Bachelors/Masters in Computer Science, Data Science, Statistics, Applied Math, or related field. Preferred certifications: AWS/Azure/GCP ML, Databricks, TensorFlow or PyTorch.

Role Overview: We are seeking a Hands-on Azure AI Architect with expertise in Azure cloud infrastructure, DevOps automation, and programming in .NET/C# or Python, combined with AI solution design and deployment. This role blends cloud architecture, AI engineering, and hands-on coding-enabling scalable AI-driven applications for industries such as banking, healthcare, retail, and digital platforms. Key Responsibilities: Architect and implement Azure-based AI solutions leveraging Azure Cognitive Services, Azure ML, and Azure OpenAI. Integrate AI/ML workloads into scalable cloud-native and hybrid infrastructures. Design and automate CI/CD and MLOps pipelines (Azure DevOps, GitHub Actions). Build automation scripts and tools using .NET/C# or Python. Manage and optimize Windows Server and Linux/Unix environments for AI workloads. Deploy and orchestrate AI-driven applications on Kubernetes/AKS. Ensure Responsible AI practices, security, compliance, and governance across AI deployments. Set up observability and monitoring frameworks for AI services (App Insights, Azure Monitor, Grafana, ELK/EFK). Collaborate with developers, data scientists, and business teams to translate AI use cases into production-grade solutions. Mentor engineers on Azure AI, DevOps, and automation best practices. Required Skills & Experience: 8-12 years of professional experience in cloud architecture, DevOps, or infrastructure engineering, with at least 3+ years in AI/ML solution design. Strong expertise in Azure AI services: Cognitive Services, Azure ML, Azure OpenAI, Synapse, Data Lake, Bot Services.