Ranking: 42
Design and build applications and services powered by Generative AI (LLMs, multimodal models, agents, synthesis models).
Develop core components such as conversational flows, autonomous agents, inference pipelines, and GenAI APIs.
Integrate foundation models with enterprise platforms and internal systems.
Design scalable, secure, and cloud-ready architectures for GenAI deployment (inference layers, gateways, APIs).
Select appropriate models (foundation, open-source, fine-tuned) based on performance, security, cost, and business alignment.
Define architectural patterns for model evaluation, observability, monitoring, guardrails, and bias control.
Define quality and evaluation frameworks, including cost, latency, and output reliability metrics.
Implement security and control mechanisms: privacy, moderation, filtering, traceability, and responsible AI usage.
Ensure compliance with corporate policies, regulatory requirements, and ethical AI standards.
Create best practices for development, testing, deployment, and scaling of GenAI solutions.
Act as a technical authority and advisor for product, engineering, data, and business teams.
Facilitate architecture reviews, technical workshops, and solution design sessions.
Document architecture blueprints, design patterns, and adoption guidelines for enterprise use.
3–5+ years developing AI, Machine Learning, or foundation-model-based solutions.
Hands-on experience building applications with LLMs and GenAI platforms (Amazon Bedrock, OpenAI, Anthropic, Vertex AI, open-source models).
Strong expertise in prompt engineering, RAG, lightweight fine-tuning, intelligent agents, and evaluation frameworks.
Experience designing APIs, microservices, and cloud architectures (AWS, GCP, or Azure).
Solid programming skills in Python, Node.js, or similar languages for GenAI services.
Experience with responsible AI tools, guardrails, moderation, and GenAI security practices
Design and build applications and services powered by Generative AI (LLMs, multimodal models, agents, synthesis models).
Develop core components such as conversational flows, autonomous agents, inference pipelines, and GenAI APIs.
Integrate foundation models with enterprise platforms and internal systems.
Design scalable, secure, and cloud-ready architectures for GenAI deployment (inference layers, gateways, APIs).
Select appropriate models (foundation, open-source, fine-tuned) based on performance, security, cost, and business alignment.
Define architectural patterns for model evaluation, observability, monitoring, guardrails, and bias control.
Define quality and evaluation frameworks, including cost, latency, and output reliability metrics.
Implement security and control mechanisms: privacy, moderation, filtering, traceability, and responsible AI usage.
Ensure compliance with corporate policies, regulatory requirements, and ethical AI standards.
Create best practices for development, testing, deployment, and scaling of GenAI solutions.
Act as a technical authority and advisor for product, engineering, data, and business teams.
Facilitate architecture reviews, technical workshops, and solution design sessions.
Document architecture blueprints, design patterns, and adoption guidelines for enterprise use.
3–5+ years developing AI, Machine Learning, or foundation-model-based solutions.
Hands-on experience building applications with LLMs and GenAI platforms (Amazon Bedrock, OpenAI, Anthropic, Vertex AI, open-source models).
Strong expertise in prompt engineering, RAG, lightweight fine-tuning, intelligent agents, and evaluation frameworks.
Experience designing APIs, microservices, and cloud architectures (AWS, GCP, or Azure).
Solid programming skills in Python, Node.js, or similar languages for GenAI services.
Experience with responsible AI tools, guardrails, moderation, and GenAI security practices
