Job description
Senior AI/ML Solution Architect - Generative AI & Agentic Systems
Algotale is a premier IT staffing and software solutions provider, delivering top-tier talent and custom-built technology to drive business success. With a strong network of skilled professionals across software development, cloud solutions, and project management, we help companies scale efficiently and execute projects seamlessly. Our flexible engagement models cater to both short-term and long-term needs, ensuring precision-matched expertise for every requirement. From IT staffing to full-cycle software development, Algotale empowers businesses with innovative, high-impact solutions.
Position Overview
We are looking for a Senior AI/ML Solution Architect with deep expertise in Generative AI and agentic systems to lead the design and implementation of enterprise-scale AI solutions. This role requires a unique blend of hands-on technical expertise in both Large Language Models (LLMs) and Small Language Models (SLMs), combined with the architectural vision to deploy these solutions across diverse computing environments.
The ideal candidate will architect scalable agentic solutions, implement advanced fine-tuning strategies, and design comprehensive integration systems that connect AI capabilities with enterprise applications. You will be at the forefront of our AI transformation initiatives, working with cutting-edge technologies while maintaining a practical approach to deployment and optimization.
Experience Requirements
Overall Experience: 8+ years in technology and software development
Generative AI Experience: 2+ years of hands-on experience with LLMs and generative AI systems
Solution Architecture Experience: 4+ years architecting enterprise-scale solutions
Key Responsibilities
Architecture & Design
Design and architect scalable agentic solutions using advanced LLM capabilities
Implement Model Context Protocol (MCP) integrations to connect applications with diverse external
services and APIs
Develop multi-agent orchestration systems for complex workflow automation
Design context and memory management systems for persistent agent interactions
Technical Implementation
Build and optimize Retrieval-Augmented Generation (RAG) systems for efficient knowledge retrieval
Implement agent frameworks (LangChain, LangGraph, Semantic Kernel, Agno) for various deployment environments
Design and deploy model inference pipelines optimized for different computing environments (cloud, edge, on-premises)
Develop comprehensive fine-tuning strategies for both Large Language Models (LLMs) and Small Language Models (SLMs)
Architect SLM deployment strategies for resource-constrained environments
Implement model compression and quantization techniques for efficient inference
Integration & Connectivity
Architect REST/gRPC/GraphQL APIs and SDK integrations for seamless service connectivity
Implement event-driven architectures using webhooks and message buses
Design secure authentication and authorization systems (SSO/OIDC)
Build connectors for popular platforms (Slack, Jira, Salesforce, CRM/ERP systems)
Data & Model Management
Design comprehensive data preprocessing pipelines including cleaning, deduplication, and PII reduction
Implement embedding creation and re-embedding strategies for optimal retrieval
Develop chunking and windowing strategies for mobile-optimized content processing
Establish model selection criteria and evaluation frameworks
Required Technical Skills
Core AI/ML Expertise
Foundation Models: Deep experience with GPT-4, Claude, LLaMA, and other state-of-the-art LLMs
Small Language Models (SLMs): Expertise in deploying and optimizing SLMs (Phi-3, Gemma, TinyLlama) for mobile environments
Agent Frameworks: Proficiency in LangChain, LangGraph, Microsoft Semantic Kernel, Agno, and custom agent development
RAG Systems: Advanced knowledge of retrieval-augmented generation, vector databases, and semantic search
Fine-tuning & Adaptation
Advanced fine-tuning techniques: LoRA/QLoRA, DoRA, AdaLoRA for parameter-efficient training
Model compression: Pruning, quantization (INT8/INT4), knowledge distillation
Prompt-tuning, adapters, prefix tuning, and P-tuning v2 methodologies
RLHF/RLAIF techniques for alignment and preference learning
Domain-specific fine-tuning for mobile use cases and vertical applications
Deployment & Optimization
SLM Deployment: Expertise in deploying Small Language Models across various computing environments
Multi-Platform Optimization: Experience optimizing both LLMs and SLMs for cloud, edge, and on- premises deployment
Efficient Inference: Knowledge of quantization (GPTQ, AWQ, GGML), pruning, and distillation techniques
Model Compression: Advanced techniques for reducing model size while maintaining performance
Real-time Processing: Expertise in streaming inference and adaptive reasoning depth control
Performance Optimization: Proficiency in autoscaling, rate limiting, and resource management
Adaptive Fine-tuning
Environment-specific model adaptation and optimization
Federated learning approaches for distributed fine-tuning
Few-shot and zero-shot learning techniques for resource-efficient adaptation
Integration Technologies
MCP Implementation: Deep understanding of Model Context Protocol for service integration
API Development: Expertise in designing and implementing REST, gRPC, and GraphQL APIs
Event Systems: Experience with event buses, webhooks, and real-time communication
Security: Knowledge of secure storage, caching, and access control systems
Development Frameworks
Libraries: TensorFlow, PyTorch, Hugging Face Transformers, LlamaIndex
Application Development: Web frameworks, desktop applications, API development
Cloud Platforms: AWS, GCP, Azure with focus on AI/ML services
DevOps: CI/CD pipelines, containerization (Docker/Kubernetes), monitoring
Preferred Qualifications
Master’s or PhD in Computer Science, AI, Machine Learning, or related field
Published research or contributions to open-source AI/ML projects
Experience with multi-modal models and cross-modal applications
Knowledge of MLOps best practices and model lifecycle management
Experience with regulatory compliance in AI systems (GDPR, AI Act, etc.)
Track record of leading AI transformation initiatives in enterprise environments
Certifications in cloud platforms (AWS, GCP, Azure) with focus on AI/ML services
Technical Competencies to Be Assessed
System design and architecture for distributed AI systems
Code review and optimization for production AI deployments
Performance benchmarking and model evaluation methodologies
Cost optimization strategies for large-scale AI deployments
Security and privacy considerations in AI systems
Scalability patterns for AI applications
