Senior Developer, Agentic AI

S&P Global Market Intelligence; Gurugram District, Hyderabad; On-site; INR 1,800,000 - 2,400,000; Full time

Job summary

S&P Global Market Intelligence seeks an AI Architect to lead autonomous AI initiatives and drive scalable deployments across cloud platforms. You will evaluate tools, design architectures, and collaborate with stakeholders to align AI strategies with business goals.

Responsibilities include integrating R&D into production, publishing architectural best practices, and ensuring robust validation, bias mitigation, and production readiness for agentic AI systems.

Qualifications

• 3-6 years of AI/ML experience with AI Architect roles.
• 2+ years designing autonomous/agentic AI systems.
• Expertise in Python.
• Hands-on with LangChain, Autogen, CrewAI.
• Cloud AI architecture on AWS and Google Cloud Vertex AI.
• MLOps pipelines (Kubeflow, MLflow).

Responsibilities

• Evaluate, recommend, and implement leading AI tools and frameworks for autonomous AI solutions.
• Drive cloud platform selection (AWS) for scalable AI deployments.
• Design strategies to optimize autonomous AI models for domain tasks.
• Spearhead integration of R&D into production architectures.
• Develop technical white papers and architectural diagrams.
• Oversee rigorous architectural testing and bias mitigation.

Skills

AI Architect

Autonomous AI

Python

ML workflows

Multi-agent AI

Education

Bachelor’s degree in CS/Math/Engineering

Tools

LangChain

Autogen

CrewAI

AWS

Vertex AI

Kubeflow

MLflow

Job description

Key Responsibilities

Framework and Platform Expertise: Evaluate, recommend, and implement leading AI tools and frameworks, with a strong focus on autonomous AI solutions (e.g., multi-agent frameworks, self-optimizing systems, LLM-driven decision engines). Drive the selection and utilization of cloud platforms (AWS preferable) for scalable AI deployments.

Customization and Optimization: Design strategies for optimizing autonomous AI models for domain-specific tasks (e.g., real-time analytics, adaptive automation). Define methodologies for fine-tuning LLMs, multi-agent frameworks, and feedback loops to align with overarching business goals and architectural principles.

Innovation and Research Integration: Spearhead the integration of R&D initiatives into production architectures, advancing agentic AI capabilities. Evaluate and prototype emerging frameworks (e.g., Autogen, AutoGPT, LangChain), neuro-symbolic architectures, and self-improving AI systems for architectural viability.

Documentation and Architectural Blueprinting: Develop comprehensive technical white papers, architectural diagrams, and best practices for autonomous AI system design and deployment. Serve as a thought leader, sharing architectural insights at conferences and contributing to open-source AI communities.

System Validation and Resilience: Design and oversee rigorous architectural testing of AI agents, including stress testing, adversarial scenario simulations, and bias mitigation strategies, ensuring alignment with compliance, ethical and performance benchmarks for robust production systems.

Stakeholder Collaboration & Advocacy: Collaborate with executives, product teams, and compliance officers to align AI architectural initiatives with strategic objectives. Advocate for AI-driven innovation and architectural best practices across the organization.

Qualifications:

• 3-6 years of progressive experience in AI/ML, with a strong track record as an AI Architect, ML Architect, or AI Solutions Lead.
• 2+ years specifically focused on designing and architecting autonomous/agentic AI systems (e.g., multi-agent frameworks, self-optimizing systems, or LLM-driven decision engines) or ML workflows.
• Expertise in Python
• Extensive hands-on experience with autonomous AI tools and frameworks: LangChain, Autogen, CrewAI, or architecting custom agentic frameworks.
• Proficiency in cloud platforms for AI architecture: AWS services - redis, lambda, eks, postgres etc., Google Cloud Vertex AI, visual studio or any other IDE with a deep understanding of their AI service offerings.
• Demonstrable experience with MLOps pipelines (e.g., Kubeflow, MLflow) and designing scalable deployment strategies for AI agents in production environments.