Key Responsibilities

- Translate business requirements into technical specifications, data models, and AI solution architectures ready for implementation.
- Develop multi-agent systems with structured role-based collaboration, agent-to-agent communication, and workflow orchestration.
- Build dynamic tool-using agents capable of runtime tool discovery, API invocation, database querying, and external system integration.
- Implement memory architectures (short-term, long-term, vector-based memory) to enable contextual continuity and learning across sessions.
- Develop Retrieval-Augmented Generation (RAG) pipelines integrating embeddings, vector databases, and contextual search for knowledge-grounded responses.
- Design, code, train, fine-tune, and deploy Machine Learning, Deep Learning, and Generative AI models using Python, PyTorch, TensorFlow, and related frameworks.
- Engineer structured prompts, function-calling workflows, guardrails, and evaluation pipelines to ensure reliable agent behavior. Develop multimodel AI pipelines integrating text, image, video, and structured data.
- Implement Model Context Protocol (MCP)-compatible connectors for standardized context sharing, tool interoperability, and modular AI integration.
- Develop adaptive reasoning frameworks with dynamic task decomposition, decision trees, and feedback-driven response refinement.
- Build event-driven and API-triggered agent workflows using Azure services and scalable backend architectures.
- Deploy and scale Agentic AI systems using Azure OpenAI, Azure ML, Docker, and Azure Kubernetes Service (AKS).
- Monitor, debug, and optimize agent performance including latency, hallucination reduction, cost efficiency, and reasoning accuracy.
- Implement safety mechanisms, access controls, logging, and responsible AI guardrails for enterprise-grade deployment.
- Create scalable data ingestion, preprocessing,



and feature engineering pipelines using Azure Databricks, Spark, and distributed data platforms.
- Implement CI/CD pipelines, model versioning, automated testing, performance tuning, and monitoring using MLflow and Azure-native tools.
- Optimize GPU utilization, inference latency, and system scalability for high-volume enterprise workloads.
- Debug, refactor, and continuously improve AI models and pipelines to ensure production reliability and performance.

Required Skills

- Strong expertise in Machine Learning, Deep Learning, Generative AI, and LLM fine-tuning using Python, PyTorch, and TensorFlow.
- Hands-on experience in Agentic AI including multi-agent orchestration, tool integration, memory architectures, dynamic reasoning workflows, and MCP-based context interoperability.
- Proven experience building RAG pipelines, embeddings, vector databases, and semantic search systems for enterprise GenAI applications.
- Robust experience with Azure (Azure OpenAI, Azure ML, Databricks, AKS) and AWS (SageMaker, Bedrock, EKS) AI ecosystems.
- Expertise in scalable data engineering and MLOps including Spark, SQL, MLflow, CI/CD, Docker, Kubernetes, and production-grade cloud deployments.

Preferred Skills

- Experience in Data Science, Applied ML, Deep Learning, or Gen AI Engineering, Agentic AI, Image Processing, MCP
- Strong experience with cloud platforms (Azure and/or AWS).
- Hands-on experience with big data and distributed systems (Spark, Kafka, Airflow).
- Experience designing production-grade GenAI and agent-based systems.
- Strong understanding of statistics, probability, optimization, and experimentation.
- Experience with NLP, Computer Vision, Time Series Forecasting, and Recommendation Systems.
- Ability to simplify complex AI concepts into clear business value propositions.
- Proven leadership, ownership, and mentoring capabilities.
- Comfortable working in fast-paced, ambiguous, and innovation-driven environments.

📌 Sr. Data Scientist (Hyderabad)
🏢 Caprus IT
📍 Hyderabad