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 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