Description
AI Soution Architect
Location: Noida, India
Department: Operations
Experience: 8
Location: Noida
Experience: 10 – 12 Years
About the Company
We are a global team of engineers, architects, designers, researchers, operators and innovators who share a passion for achieving client goals. Our engineering services help businesses thrive at the intersection of technology and people. From the latest AI implementations to legacy platform migrations and everything in between, our services span the enterprise technology spectrum. Our world class experience transformation playbook elevates digital success and increases ROI with a relentless focus on the human experience. Our customer base includes Fortune 500 companies around the globe. We’ve got the skills and insights and we’re also fun to work with. Our global team spans a diverse cultural spectrum, with wide ranging interests, enabling us to bring personality and depth to every engagement.
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Role Overview
We are seeking a highly skilled AI Solution Architect to lead the design, development, and deployment of scalable AI-driven systems across Machine Learning (ML), Generative AI (GenAI), and data platforms. This role requires deep expertise in architecting production-grade AI systems, building Retrieval-Augmented Generation (RAG) pipelines, and integrating AI into enterprise applications.
The ideal candidate will combine strong hands-on engineering capabilities with architectural thinking, enabling end-to-end ownership—from problem definition and model development to deployment, scaling, and optimization.
Responsibilities
AI/ML & GenAI Architecture
Design end-to-end AI architectures for use cases such as semantic search, recommendation systems, conversational AI, forecasting, and intelligent automation
Architect and implement RAG-based systems using LLMs, embedding models, vector databases, and orchestration frameworks
Define system design patterns for multi-agent systems, tool-augmented LLMs, and agentic workflows
Model Development & Optimization
Develop, fine-tune, and evaluate ML/DL models for classification, regression, ranking, and recommendation
Optimize LLM pipelines for latency, cost, and accuracy (prompt engineering, caching, retrieval strategies, model selection)
Implement evaluation frameworks for LLM outputs (hallucination detection, grounding, relevance scoring)
Data Engineering & Pipelines
Build scalable data pipelines using PySpark, Spark, or distributed systems
Design feature engineering pipelines and data preprocessing workflows for structured and unstructured data
Work with real-time and batch data pipelines (Kafka, streaming frameworks)
Search & NLP Systems
Build hybrid search systems combining:
Lexical search (BM25, Elasticsearch/OpenSearch)
Semantic search (embeddings, ANN search)
Design NLP pipelines for entity extraction, summarization, classification, and Q&A systems
Deployment & MLOps
Build and deploy APIs for model serving using FastAPI, Flask, or similar frameworks
Implement CI/CD pipelines for ML systems, including versioning, monitoring, and rollback strategies
Deploy models using Docker, Kubernetes, and cloud-native services
Monitor model performance, drift, and system health in production
Cloud & Platform Engineering
Architect AI systems on AWS/GCP/Azure, leveraging services like:
AWS SageMaker, Bedrock
GCP Vertex AI
Managed vector databases and data lakes
Design scalable, fault-tolerant, and cost-efficient cloud architectures
Collaboration & Leadership
Translate business problems into scalable AI solutions and technical designs
Work closely with product, engineering, and data teams to deliver production-ready systems
Mentor engineers and establish best practices for AI/ML engineering and architecture
Required Technical Skills
Core Programming & Engineering
Strong proficiency in Python (advanced OOP, async programming, performance optimization)
Experience building production-grade backend systems and APIs
Machine Learning & Deep Learning
Hands-on experience with PyTorch / TensorFlow / Scikit-learn
Strong understanding of:
Supervised & unsupervised learning
Model evaluation metrics
Feature engineering and model tuning
Generative AI & LLMs
Hands-on experience with:
OpenAI, Anthropic, or open-source LLMs (LLaMA, Mistral, etc.)
Prompt engineering and prompt optimization
RAG pipelines and embedding models
Experience with vector databases (Pinecone, Weaviate, FAISS, Milvus)
Search & Retrieval
Experience with Elasticsearch/OpenSearch
Knowledge of hybrid retrieval architectures and ranking strategies
Data & Distributed Systems
Strong experience with NumPy, Pandas, PySpark
Experience working with large-scale datasets and distributed processing systems
MLOps & Deployment
Experience with:
FastAPI / Flask for serving models
Docker, Kubernetes
CI/CD pipelines (GitHub Actions, Jenkins, etc.)
Familiarity with model monitoring, logging, and observability tools
Cloud Platforms
Hands-on experience with AWS / GCP / Azure
Understanding of cloud-native AI/ML services and infrastructure
Good to Have
Experience with LangChain, LangGraph, Dify, LlamaIndex, or agent frameworks
Experience designing multi-agent or autonomous AI systems
Knowledge of Graph databases (Neo4j) and knowledge graphs
Experience with streaming systems (Kafka, Flink)
Exposure to Computer Vision, Time-Series Forecasting, or Reinforcement Learning
Experience in cost optimization and scaling GenAI systems in production
Who will succeed in this role
Candidates who have built and deployed real-world AI systems (not just experimentation)
Strong exposure to GenAI + traditional ML combination
Experience in architecture/design roles (not only individual contributor model building)
Hands-on experience with RAG, vector DBs, and LLM orchestration frameworks
Candidates from product companies, AI startups, or platform engineering teams preferred
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