Senior Gen AI Engineer

2 days ago

Hyderabad, Telangana, India Soothsayer Analytics Full time US$ 1,20,000 - US$ 2,00,000 per year

We seek a Senior Gen AI Engineer with strong ML fundamentals and data engineering expertise to lead scalable AI/LLM solutions. This role focuses on integrating AI models into production, optimizing machine learning workflows, and creating scalable AI-driven systems. You will design, fine-tune, and deploy models (e.g., LLMs, RAG architectures) while ensuring robust data pipelines and MLOps practices.

Key Responsibilities

Agentic AI & Workflow Design: Lead design and implementation of Agentic AI systems and multi-step AI workflows. Build AI orchestration systems using frameworks like LangGraph. Utilize Agents, Tools, and Chains for complex task automation. Implement Agent-to-Agent (A2A) communication and Model Connect Protocol (MCP) for inter-model interactions.

Production MLOps & Deployment: Develop, train, and deploy ML models optimized for production. Implement CI/CD pipelines (GitHub), automated testing, and robust observability (monitoring, logging, tracing) for Gen AI solutions. Containerize models (Docker) and deploy on cloud (AWS / Azure/ GCP) using Kubernetes. Implement robust AI/LLM security measures and adhere to Responsible AI principles.

AI Model Integration: Integrate LLMs and models from HuggingFace. Apply deep learning concepts with PyTorch or TensorFlow.

Data & Prompt Engineering: Build scalable data pipelines for unstructured/text data. Design and implement embedding/chunking strategies for scalable data processing. Optimize storage/retrieval for embeddings (e.g., Pinecone, Weaviate). Utilize Prompt Engineering techniques to fine-tune AI model performance.

Solution Development: Develop GenAI-driven Text-to-SQL solutions.

Programming: Python.

Foundation Model APIs: AzureOpenAI, OpenAI, Gemini, Anthropic, or AWS Bedrock.

Agentic AI & LLM Frameworks: LangChain, LangGraph, A2A, MCP, Chains, Tools, Agents. Ability to design multi-agent systems, autonomous reasoning pipelines, and tool-calling capabilities for AI agents.

MLOps/LLMOps: Docker , Kubernetes (K8s) , CI/CD , Automated Testing, Monitoring, Observability, Model Registries, Data Versioning.

Cloud Platforms: AWS/Azure/GCP.

Vector Databases: Pinecone, Weaviate, or similar leading platforms.

Prompt Engineering.

Security & Ethics: AI/LLM solution security, Responsible AI principles.

Version Control: GitHub.

Databases: SQL/NoSQL.

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