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:

1. Design and architect end-to-end agentic AI solutions that can reason, plan, and execute complex multi-step tasks autonomously
2. Develop architectural blueprints for multi-agent systems with proper coordination, communication, and conflict resolution mechanisms
3. Create scalable frameworks for agent orchestration, task delegation, and workflow automation
4. Design robust memory architectures including episodic, semantic, and procedural memory systems for agents
5. Lead cross-functional teams in implementing agentic AI solutions from conception to production deployment
6. Establish best practices for agentic system development, testing, and maintenance
7. Mentor junior engineers on advanced AI concepts, agent design patterns, and MLOps practices
8. Architect and implement LLM deployment strategies for both cloud and on-premise environments
9. Design and execute model quantization, pruning, and optimization techniques for efficient inference
10. Implement knowledge distillation pipelines to create specialized smaller models from large foundation models
11. Develop model versioning, A/B testing, and gradual rollout strategies for production systems
12. Design and implement various agentic patterns including ReAct, Chain-of-Thought, Tree-of-Thoughts, and multi-agent collaboration
13. Architect tool-using agents with proper API integration, error handling, and safety constraints
14. Develop planning and reasoning engines for complex task decomposition and execution
15. 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