Solutions Architect- AI

Position Overview:We are seeking a skilled Solution Architect specializing in Agentic AI to lead the design, development, and implementation of intelligent autonomous systems. The ideal candidate will bridge the gap between cutting-edge AI research and practical enterprise solutions, architecting robust agentic frameworks that can operate independently while maintaining human oversight and control.Key Responsibilities:Design and architect end-to-end agentic AI solutions that can reason, plan, and execute complex multi-step tasks autonomouslyDevelop architectural blueprints for multi-agent systems with proper coordination, communication, and conflict resolution mechanismsCreate scalable frameworks for agent orchestration, task delegation, and workflow automationDesign robust memory architectures including episodic, semantic, and procedural memory systems for agentsLead cross-functional teams in implementing agentic AI solutions from conception to production deploymentEstablish best practices for agentic system development, testing, and maintenanceMentor junior engineers on advanced AI concepts, agent design patterns, and MLOps practicesArchitect and implement LLM deployment strategies for both cloud and on-premise environmentsDesign and execute model quantization, pruning, and optimization techniques for efficient inferenceImplement knowledge distillation pipelines to create specialized smaller models from large foundation modelsDevelop model versioning, A/B testing, and gradual rollout strategies for production systemsDesign and implement various agentic patterns including ReAct, Chain-of-Thought, Tree-of-Thoughts, and multi-agent collaborationArchitect tool-using agents with proper API integration, error handling, and safety constraintsDevelop planning and reasoning engines for complex task decomposition and executionImplement self-reflection and self-correction mechanisms for autonomous error recoveryExperience & Education:8+ years of hands-on experience in Machine Learning, Deep Learning, and Data EngineeringBachelor's or Master's degree in Computer Science, AI/ML, Data Science, or related fieldProven track record of deploying ML/AI solutions in production environmentsCore Technical Skills:Python Expertise: Advanced proficiency in Python with deep understanding of ML libraries (PyTorch, TensorFlow, scikit-learn, Transformers, Hugging Face)Machine Learning & Deep Learning Expertise: Good hands-on experience on training the ML workloads, Fine-Tune the Models, Versioning with A/B TestingCloud Expertise: Must have either of any cloud (Azure/AWS/GCP) Expertise to drive the ML initiativesLLM Mastery: Comprehensive knowledge of Large Language Models including:Foundation models (GPT, Claude, LLaMA, Gemini, etc.)Fine-tuning techniques (LoRA, QLoRA, PEFT methods)Prompt engineering and optimization strategiesModel evaluation and benchmarking methodologiesAgentic AI SpecializationAgent Design Patterns: Hands-on experience implementing:ReAct (Reasoning + Acting) agentsMulti-agent systems and coordination protocolsTool-using agents and API integrationPlanning and goal-oriented agentsConversational and task-oriented agentsMemory & Caching Systems: Deep understanding of:Vector databases and semantic search (Pinecone, Weaviate, Chroma)Memory architectures (short-term, long-term, episodic memory)Caching strategies for LLM inference optimization; Redis CacheContext window management and memory consolidationHuman-in-the-Loop (HITL) Systems: Experience designing:Human oversight and intervention mechanismsApproval workflows and escalation protocolsFeedback collection and model improvement loopsTrust and transparency frameworksDeployment & InfrastructureModel Deployment: Expertise in:LLM quantization techniques (GPTQ, GGML, AWQ)Model serving frameworks (vLLM, TensorRT-LLM, Triton)Container orchestration (Docker, Kubernetes)Both cloud (AWS/Azure) and on-premise deploymentsAPI Development: Proficiency in:FastAPI framework for high-performance API developmentAsync/await patterns for concurrent processingWebSocket implementations for real-time interactionsAPI security, rate limiting, and monitoringAdvanced Technical Requirements:Emerging AI TechnologiesExperience with multi-modal AI systems (vision-language models, audio processing, video processing)Knowledge of Ethical AI and AI safety techniquesAdvanced Agentic PatternsMulti-Agent Orchestration: Experience with agent communication protocols, consensus mechanisms, and distributed decision-makingMeta-Learning Agents: Implementation of agents that can learn how to learn and adapt to new tasks quicklyHierarchical Planning: Design of agents with multiple levels of abstraction for complex task executionSelf-Modifying Agents: Understanding of agents that can modify their own code or parametersSoft Skills & LeadershipCommunication: Ability to explain complex AI concepts to both technical and non-technical stakeholdersProblem-Solving: Strong analytical and creative problem-solving abilitiesLeadership: Experience leading technical teams and driving architectural decisionsAdaptability: Ability to stay current with rapidly evolving AI landscapeEthics: Strong understanding of AI ethics and responsible AI development practices