Design, develop, and deploy multi-agent systems and agentic applications using frameworks like AutoGen, LangGraph, CrewAI, or similar
Build intelligent workflow orchestration systems that enable autonomous decision-making and task execution
Implement Agent-to-Agent (A2A) communication protocols and Model Context Protocol (MCP) for seamless agent collaboration
Develop automation solutions using OpenAPI standards for integration with enterprise systems
Create self-healing, adaptive workflows that optimize business processes autonomously
Use ML, deep learning, and Generative AI tools to design, evangelize, and implement state-of-the-art solutions
Define and implement best practices for building, testing, and deploying scalable AI solutions, with a focus on generative models and LLMs using proprietary or open-source models
Drive successful business outcomes by designing and building cloud-hosted Generative AI solutions
Work closely with internal teams to integrate RAG workflows, agent-based systems, and automation frameworks into applications
Design and implement architectural solutions for Information Retrieval using RAG, Vector DBs, and Knowledge Graphs
Work with public cloud (AWS) and on-premises infrastructure for deploying LLMs, agents, and orchestration systems
Evaluate, build, and fine-tune ML models and LLMs to solve complex business problems
Stay abreast of latest developments in agentic AI, autonomous systems, language models, and generative AI technologies.
BE, Master's or Ph.D. in Computer Science, Artificial Intelligence, Machine Learning, or equivalent practical experience
4-8 years of overall technical experience with 0-2 years of hands-on experience in Generative AI and LLM technologies
1+ years of experience building agentic systems, workflow automation, or autonomous AI applications
Deep hands-on experience with agentic frameworks (AutoGen, LangGraph, CrewAI, Agency Swarm, or similar)
Strong knowledge of workflow orchestration tools and patterns (Temporal, Airflow, Prefect, or similar)
Expertise in OpenAPI standards, Agent-to-Agent (A2A) protocols, and Model Context Protocol (MCP)
Experience designing multi-agent architectures with memory, planning, and tool-use capabilities
Knowledge of agent evaluation, testing frameworks, and observability patterns
Proven track record of deploying and optimizing LLM models for inference in production environments
Extensive experience with LLM orchestration frameworks (LangChain, LlamaIndex required)
Hands-on experience with Amazon Bedrock, SageMaker JumpStart, and other cloud-based LLM platforms
Expertise in RAG architectures, Fine-tuning techniques, and Prompt Engineering
Deep understanding of Vector Databases (Pinecone, Weaviate, Milvus, ChromaDB) and Knowledge Graphs
Expert in NLP techniques and deep learning libraries (Transformer models, LSTM, BiLSTM, CNN, BERT, GPT, T5)
Proficiency with ML frameworks: TensorFlow, PyTorch, Hugging Face Transformers, scikit-learn
Strong programming skills in Python (required), plus JavaScript/TypeScript or Node.js
Deep understanding of data structures, algorithms, and system design patterns
Hands-on experience in MLOps/LLMOps including data pipelines, model training/refinement, validation, drift management, and serving
Experience with containerization (Docker, Kubernetes) and CI/CD pipelines for ML systems
Knowledge of monitoring, logging, and observability tools for production AI systems.
