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Solutions Architect- AI

Solutions Architect- AI

Tata TechnologiesMumbai, Maharashtra, India
6 hours ago
Job description

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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