Role:
AI Solution Architect
Location:
India, Remote
Experience:
8-12 Years
Algoworks
www.algoworks.com
About the company
Algoworks is an award-winning artificial intelligence, engineering services and experience transformation firm with offices across the United States, Europe, South America and India. We bring together a global team of engineers, architects, designers, researchers and operators united by rigor, accountability and a commitment to delivering measurable results.
For over 20 years, Algoworks has partnered with Fortune 500 organizations across the Americas, Europe and Asia to define, build and run technology that drives meaningful business outcomes. Our work combines human-centered design, engineering excellence and AI-powered capabilities to solve complex challenges with clarity and precision. Innovation, particularly in the responsible application of AI, is embedded in how teams approach problem-solving and continuous improvement.
At Algoworks, growth is continuous and closely tied to impact. Teams collaborate across geographies and disciplines, strengthening outcomes through shared insight and collective expertise. The culture values transparency, open dialogue and an environment where every voice is heard and contribution is recognized.
Through collaboration, accountability and a focus on results, Algoworks operates at the intersection of technology and people, building not only advanced systems but strong global teams that elevate performance and create lasting impact.
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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 capabilities into enterprise applications. The ideal candidate combines strong hands-on engineering skills with architectural leadership, enabling end-to-end ownership—from problem definition to deployment and optimization.
Key responsibilities:
1.AI/ML and 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 design patterns for multi-agent systems, tool-augmented LLMs, and agentic workflows.
2.Model development and optimization
Develop, fine-tune, and evaluate ML/DL models for classification, regression, ranking, and recommendation use cases.
Optimize LLM pipelines for latency, cost, and accuracy through prompt engineering, caching, and retrieval strategies.
Implement evaluation frameworks for LLM outputs (hallucination detection, grounding, relevance scoring).
3.Data engineering and pipelines
Build scalable data pipelines using PySpark, Spark, or distributed systems.
Design feature engineering and data preprocessing workflows for structured and unstructured data.
Work with real-time and batch data processing systems (Kafka, streaming frameworks).
4.Search and NLP systems
Design hybrid search systems combining lexical (BM25, Elasticsearch/OpenSearch) and semantic (embeddings, ANN) approaches.
Build NLP pipelines for entity extraction, summarization, classification, and Q&A systems.
5.Deployment and 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.
6.Cloud and platform engineering
Architect AI systems on AWS, GCP, or Azure using services such as SageMaker, Bedrock, and Vertex AI.
Design scalable, fault-tolerant, and cost-efficient cloud architectures.
Leverage managed vector databases and data lake architectures.
7. Collaboration and leadership
Translate business problems into scalable AI solutions and technical designs.
Collaborate 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 and engineering
Strong proficiency in Python (advanced OOP, async programming, performance optimization).
Experience building production-grade backend systems and APIs.
Machine learning and deep learning
Hands-on experience with PyTorch, TensorFlow, or Scikit-learn.
Strong understanding of supervised/unsupervised learning, evaluation metrics, feature engineering, and model tuning.
Generative AI and LLMs
Experience with OpenAI, Anthropic, or open-source LLMs (LLaMA, Mistral, etc.).
Strong expertise in prompt engineering, RAG pipelines, and embedding models.
Experience with vector databases (Pinecone, Weaviate, FAISS, Milvus).
Search and retrieval
Experience with Elasticsearch/OpenSearch.
Understanding of hybrid retrieval and ranking strategies.
Data and distributed systems
Strong experience with NumPy, Pandas, and PySpark.
Experience handling large-scale datasets and distributed processing.
MLOps and deployment
Experience with FastAPI/Flask for model serving.
Hands-on experience with Docker, Kubernetes, and CI/CD pipelines (GitHub Actions, Jenkins, etc.).
Familiarity with model monitoring, logging, and observability.
Cloud platforms
Hands-on experience with AWS, GCP, or Azure.
Understanding of cloud-native AI/ML services and infrastructure.
Qualifications
Bachelor’s or master’s degree in computer science, AI, or related field.
Good to have skills:
Experience with LangChain, LangGraph, Dify, LlamaIndex, or similar 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.
Must have skills:
Strong expertise in AI/ML architecture, LLMs, RAG systems, and scalable enterprise AI solutions.
Advanced proficiency in Python, backend/API development, and production-grade system design.
Hands-on experience with ML frameworks, vector databases, search systems, and distributed data processing.
Strong experience with MLOps, cloud platforms, Docker, Kubernetes, and CI/CD for AI deployment.
Desired attributes:
Strong architectural thinking with hands-on execution capability.
Ability to operate in ambiguity and drive end-to-end ownership.
Excellent problem-solving and analytical skills.
Strong communication and stakeholder management.
Passion for innovation in AI/ML and continuous learning.
Who will succeed in this role:
Candidates who have built and deployed real-world AI systems (beyond experimentation).
Professionals with strong exposure to both Generative AI and traditional ML.
Individuals with prior architecture/design experience (not limited to IC model development).
Hands-on expertise in RAG pipelines, vector databases, and LLM orchestration frameworks.
Candidates from product companies, AI startups, or platform engineering environments preferred.
Interview process
2 rounds of discussion.
