Key Responsibilities
Solution Architecture & Design
Design end-to-end Generative AI architectures using LLMs, RAG, AI Agents, vector databases, and multimodal models.
Define scalable, secure, and cost-effective AI solution architectures.
Create technical blueprints, architecture diagrams, and deployment strategies.
Evaluate and recommend AI platforms, frameworks, and cloud services.
AI & LLM Engineering
Architect solutions using models such as GPT, Claude, Gemini, Llama, Mistral, and open-source LLMs.
Design prompt engineering, fine-tuning, and model optimization strategies.
Build RAG pipelines using vector databases such as Pinecone, Weaviate, Chroma, or Azure AI Search.
Implement AI agents and orchestration frameworks such as LangChain, LangGraph, CrewAI, or Semantic Kernel.
Cloud & Platform Architecture
Design AI solutions on AWS, Azure, or Google Cloud.
Utilize services such as Azure OpenAI, AWS Bedrock, Vertex AI, and AI Foundry.
Architect MLOps and LLMOps pipelines for deployment, monitoring, and governance.
Ensure high availability, scalability, and performance.
Business & Stakeholder Engagement
Gather business requirements and translate them into technical solutions.
Conduct client workshops, discovery sessions, and architecture reviews.
Support pre-sales activities, RFP responses, solution demonstrations, and proposal creation.
Present AI strategies and solution roadmaps to executives and stakeholders.
Governance, Security & Compliance
Establish responsible AI practices and governance frameworks.
Address model security, privacy, compliance, and risk management.
Define monitoring, observability, and evaluation frameworks for AI systems.
Ensure alignment with enterprise architecture standards.
Leadership & Mentorship
Guide data scientists, ML engineers, and software development teams.
Review solution designs and implementation approaches.
Stay current with emerging Gen AI technologies and industry trends.
Drive innovation and AI adoption across the organization.
Technical Skills
Generative AI, LLMs, RAG, AI Agents
Machine Learning and Deep Learning
Python, SQL, REST APIs
LangChain, LangGraph, LlamaIndex, CrewAI
Vector Databases (Pinecone, Weaviate, Chroma, Milvus)
AWS, Azure, GCP
Docker, Kubernetes, CI/CD
MLOps / LLMOps
Data Architecture and Data Engineering
AI Security and Governance
Soft Skills
Solution design and architecture thinking
Client-facing communication
Stakeholder management
Leadership and mentoring
Problem-solving and decision-making
Presentation and consulting skills
Qualifications
Bachelor’s or Master’s degree in Computer Science, Artificial Intelligence, Data Science, or a related field.
10–15+ years of software engineering, cloud architecture, or AI experience.
3–5+ years of experience in AI/ML solution architecture.
Relevant certifications preferred
AWS Solutions Architect
Azure AI Engineer / Azure Solutions Architect
Google Professional Cloud Architect
Databricks or Snowflake certifications
If you like wild growth and working with happy, enthusiastic over-achievers, you'll enjoy your career with us!
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