Role Overview We re seeking a seasoned Data Scientist with deep expertise in AI/ML and a proven track record of delivering scalable solutions. This role blends hands-on model development with strategic leadership, enabling you to shape the future of intelligent systems across NLP, GenAI, and MLOps.

- AI/ML Model Development Optimization Build and optimize models using PyTorch, TensorFlow, and cutting-edge ML frameworks. Apply prompt engineering and fine-tuning techniques to foundation models (e.g., GPT-4, Claude, Llama). Deliver NLP solutions: classification, summarization, sentiment analysis, entity extraction, conversational AI, and generative workflows. Architect Retrieval-Augmented Generation (RAG) pipelines using vector databases, smart chunking, ranking, and caching. Develop multi-agent systems for task planning, decomposition, and tool orchestration.
- MLOps Productionization Implement robust MLOps pipelines: CI/CD, model monitoring, feature stores, and governance. Ensure reproducibility, drift detection, explainability (SHAP, LIME), and responsible AI compliance. Optimize inference performance and design rollback strategies for production resilience.
- Performance, Security Compliance Ensure AI solutions meet security, compliance, and performance benchmarks. Integrate external APIs with cost-aware and latency-optimized strategies. Maintain observability and traceability across deployed systems.
- Stakeholder Engagement Strategic Roadmapping Translate business goals into scalable technical architectures. Communicate risks, metrics, and impact to executives and cross-functional stakeholders. Create design diagrams, runbooks, and model cards; lead workshops and technical deep-dives.

Technology Stack

Programming Frameworks Expert: Python Libraries: Scikit-learn, Pandas, NumPy, XGBoost, LightGBM, NLTK, Frameworks: PyTorch, TensorFlow/Keras, Hugging Face, LangChain, LlamaIndex, Ray, PyTorch Lightning, FastAPI, Flask API Integration: RESTful APIs, OAuth, API keys,



paginationCloud DevOps Cloud: AWS, GCP, Azure Containers: Docker Orchestration: Kubernetes (EKS/GKE/AKS) IaC (nice-to-have): Terraform, CloudFormationMLOps Tools Experiment Tracking: DVC, Weights Biases, Neptune, TensorBoard Feature Stores, Model Registry, CI/CD pipelinesDatabases Relational: PostgreSQL, MySQL NoSQL: MongoDB, DynamoDB Vector Stores: FAISS, pgvector, Pinecone, OpenSearch, Milvus, WeaviateRAG Multi-Agent Systems Components: Document loaders, semantic splitters, embeddings (OpenAI, Cohere, Vertex AI), hybrid/BM25 retrievers, Cross-Encoder rerankers Frameworks: Crew AI, AutoGen, LangGraph, MetaGPT, Haystack AgentsSecurity Compliance Secure AI development, privacy-preserving techniques, bias detection, model explainabilityLeadership Pre-Sales Experience Proven success in shipping ML products at scale. Lead client workshops, technical discovery, and early-stage solutioning. Support pre-sales by identifying scalable patterns and architectural differentiators.

Qualifications 7-8 years in software engineering/data science, with 5+ years leading AI/ML projects. Bachelor s or Master s in Computer Science, AI, Data Science, or related field. Preferred Certifications: AWS Certified Machine Learning Google Professional ML Engineer Azure AI Engineer Associate Kubernetes CKA/CKADSoft Skills Leadership Attributes Influential Communication: Translate complex ML concepts for non-technical audiences; robust storytelling and presentation skills. Mentorship Mindset: Inspire and coach cross-functional teams; foster psychological safety. Bias for Action: Rapid prototyping, iterative development, and production delivery. Critical Thinking: Hypothesis-driven experimentation, A/B testing, and data-informed decisions. Adaptability: Stay ahead of GenAI trends, tools, and emerging research.

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📌 Data Scientist AI/ML (Karnataka)
🏢 Aziro
📍 Karnataka