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 autonomously Develop architectural blueprints for multi-agent systems with proper coordination, communication, and conflict resolution mechanisms Create scalable frameworks for agent orchestration, task delegation, and workflow automation Design robust memory architectures including episodic, semantic, and procedural memory systems for agents Lead cross-functional teams in implementing agentic AI solutions from conception to production deployment Establish best practices for agentic system development, testing, and maintenance Mentor junior engineers on advanced AI concepts, agent design patterns, and MLOps practices Architect and implement LLM deployment strategies for both cloud and on-premise environments Design and execute model quantization, pruning, and optimization techniques for efficient inference Implement knowledge distillation pipelines to create specialized smaller models from large foundation models Develop model versioning, A/B testing, and gradual rollout strategies for production systems Design and implement various agentic patterns including ReAct, Chain-of-Thought, Tree-of-Thoughts, and multi-agent collaboration Architect tool-using agents with proper API integration, error handling, and safety constraints Develop planning and reasoning engines for complex task decomposition and execution Implement self-reflection and self-correction mechanisms for autonomous error recoveryExperience & Education:8+ years of hands-on experiencein Machine Learning, Deep Learning, and Data Engineering Bachelor's or Master's degree in Computer Science, AI/ML, Data Science, or related field Proven 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 Testing Cloud Expertise:Must have either of any cloud (Azure/AWS/GCP) Expertise to drive the ML initiatives LLM 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 strategies Model evaluation and benchmarking methodologies Agentic AI Specialization Agent Design Patterns : Hands-on experience implementing: ReAct (Reasoning + Acting) agents Multi-agent systems and coordination protocols Tool-using agents and API integration Planning and goal-oriented agents Conversational and task-oriented agents Memory & 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 Cache Context window management and memory consolidation Human-in-the-Loop (HITL) Systems : Experience designing: Human oversight and intervention mechanisms Approval workflows and escalation protocols Feedback collection and model improvement loops Trust and transparency frameworks Deployment & Infrastructure Model 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 deployments API Development : Proficiency in: FastAPI framework for high-performance API development Async/await patterns for concurrent processing WebSocket implementations for real-time interactions API security, rate limiting, and monitoringAdvanced Technical Requirements:Emerging AI Technologies Experience with multi-modal AI systems (vision-language models, audio processing, video processing) Knowledge of Ethical AI and AI safety techniquesAdvanced Agentic Patterns Multi-Agent Orchestration : Experience with agent communication protocols, consensus mechanisms, and distributed decision-making Meta-Learning Agents : Implementation of agents that can learn how to learn and adapt to new tasks quickly Hierarchical Planning : Design of agents with multiple levels of abstraction for complex task execution Self-Modifying Agents : Understanding of agents that can modify their own code or parametersSoft Skills & Leadership Communication : Ability to explain complex AI concepts to both technical and non-technical stakeholders Problem-Solving : Strong analytical and creative problem-solving abilities Leadership : Experience leading technical teams and driving architectural decisions Adaptability : Ability to stay current with rapidly evolving AI landscape Ethics : Strong understanding of AI ethics and responsible AI development practices