Solutions Architect- AI

2 weeks ago


Mumbai, India Tata Technologies Full time

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 recovery Experience & Education: - 8+ years of hands-on experience in 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 environments Core 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 monitoring Advanced 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 techniques Advanced 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 parameters Soft 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


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