Role Description
- 6+ years of skilled experience in AI/ML development, software engineering, or testing.
- Hands-on experience with Copilot, Claude, OpenAI, or other LLM-based platforms.
- Dedicated testing and QA experience within AI/ML systems.
- Proven experience delivering production-grade AI applications.
- Experience working with multi-model AI ecosystems across OpenAI, Anthropic, Microsoft, Google, Meta, and similar platforms.
- Hands-on experience in designing, developing, and deploying AI-powered tools and applications in enterprise environments.
- Demonstrated experience implementing and managing productionized AI solutions, including monitoring, governance, scalability, and performance optimization.
- Experience implementing RAG (Retrieval Augmented Generation) solutions and integrating with vector databases.
- Exposure to MCP (Model Context Protocol) servers and AI agent frameworks.
AI &
- Machine Learning Technologies
- LangChain
- LlamaIndex
- OpenAI APIs
- Anthropic APIs
- Azure OpenAI
- Microsoft Copilot
- Prompt Engineering
- RAG (Retrieval Augmented Generation)
- MCP (Model Context Protocol) Servers
- AI Guardrails implementation and validation
- Context Compression and Token Optimization techniques
Key Responsibilities
AI Development &
- Implementation
- Build and maintain enterprise-grade AI tools leveraging LLMs, Copilots, and AI agent frameworks.
- Implement AI Guardrails to ensure application safety, compliance, responsible AI usage, and security.
- Design and optimize Context Compression strategies to reduce token consumption, improve performance, and control operational costs.
- Develop and integrate RAG pipelines and MCP Server-based solutions to enable contextual and scalable AI experiences.
- Deploy, monitor, and maintain productionized AI solutions with appropriate observability, governance,
and feedback mechanisms.
The Candidate Should Possess a Strong Understanding Of
- Large Language Models (LLMs)
- Generative AI solution architecture
- Retrieval Augmented Generation (RAG)
- Vector Databases
- NLP/NLU concepts
- AI Ethics and Responsible AI
- Bias Detection and Mitigation
- AI Security and Data Privacy
- Regulatory Compliance Requirements
- AI Guardrails, content filtering, prompt injection prevention, and responsible AI controls
- Context Compression, token management, and cost optimization strategies
- MCP (Model Context Protocol) architecture and AI agent integrations
- Best practices for production deployment, monitoring, and governance of AI solutions
Required Technical Competencies
AI &
- LLM Expertise
- Microsoft Copilot Suite
- GitHub Copilot
- Claude (Opus, Sonnet, Haiku)
- OpenAI GPT Models
- Prompt Engineering
- Few-shot Learning Techniques
- Model Selection &
- Evaluation
- Hands-on experience building AI tools and enterprise AI applications
- Design and implementation of AI Guardrails
- Context Compression and Token Optimization techniques
- RAG architecture design and implementation
- MCP Server integration and configuration
- Production deployment and lifecycle management of AI solutions
- Experience implementing AI Guardrails and Responsible AI frameworks in enterprise environments.
- Hands-on experience optimizing LLM applications for token efficiency, latency, and cost management.
- Experience working with MCP servers, AI agents, and multi-agent architectures.
- Experience deploying and supporting productionized AI solutions at scale.
- Experience building custom AI copilots, assistants, and enterprise AI tools.
Skills
LangChain, LLMs, MLOps, Python, LlamaIndex, OpenAI, NoSQL, NLP, SQL, Prompt Engineering, Machine Learning, AI development, Guardrails, RAG
📌 AI Engineer (Hyderabad)
🏢 UST
📍 Hyderabad