AI Agent Engineer – AI Consulting & Strategy

Experience : 5+ Years Experience
Education : Bachelor’s/Master’s degree
Work Location : Chennai, India (Chennai/Remote/Hybrid)

Key Responsibilities :

• Build and maintain a centralized Agent Library for reusable components, prompts, and integration adapters.
• Develop agent frameworks and orchestration logic for multi-agent collaboration scenarios.
• Design integration connectors to institutional systems like ERP, SIS, CRM, or document repositories.
• Collaborate with LLM Engineers to improve agent reasoning, memory, and retrieval accuracy.
• Define agent governance frameworks, telemetry, and safety monitoring mechanisms.
• Implement agentic testing frameworks to ensure consistency, explainability, and reliability.
• Continuously benchmark and optimize agent workflows for latency, accuracy, and contextual relevance.

Required Technical Skills :

Agent Frameworks & Architecture

• Expertise in designing and implementing autonomous or semi-autonomous AI agents using frameworks like LangChain, AutoGen, CrewAI, or Semantic Kernel.
• Experience with multi-agent systems, memory management, and tool orchestration.
• Strong understanding of agent life cycle design, task delegation, and context management.

Reusable Agent Components

• Develop modular agent templates, skills, and toolkits that can be easily configured for multiple workflows.
• Build and maintain an enterprise Agent Library with metadata tagging, versioning, and documentation.
• Create reusable connectors for integrating agents with APIs, databases, and business systems.

Integration & Orchestration

• Design workflow orchestration patterns integrating agents with enterprise platforms (ERP, CRM, LMS, or HR systems).
• Implement event-driven architectures using message queues, APIs, or webhook triggers.
• Collaborate with DevOps/MLOps to enable continuous deployment and scaling of AI agents.

LLM & Context Management

• Integrate LLMs (OpenAI, Anthropic, Mistral, or local models) into agent reasoning pipelines.
• Manage context caching, vector retrieval, and memory stores for long-term agent context retention.
• Optimize prompt construction, chaining logic, and tool selection for efficient reasoning.

Governance, Security & Observability

• Implement guardrails, policy-based control, and access-level governance for enterprise AI agents.
• Set up agent telemetry and logging using observability tools (Prometheus, OpenTelemetry, Datadog, or Grafana).
• Ensure agents comply with data security, compliance, and auditability standards.

Programming & Infrastructure

• Strong development skills in Python, TypeScript, or Go, with emphasis on modular, testable design.
• Familiarity with containerization (Docker, Kubernetes) and cloud environments (AWS, Azure, GCP, or OCI).
• Experience with API design, GraphQL, and asynchronous event-driven programming.

Required Skills & Experience :

• 5+ years of experience in AI/ML application development or enterprise integration engineering.
• Proven experience in agent framework development or LLM orchestration systems.
• Deep understanding of API-driven architectures, event streaming, and data integration.
• Hands-on experience with LangChain, AutoGen, CrewAI, or similar frameworks.
• Strong coding and automation skills with a focus on reusability and scalability.
• Experience in testing, evaluating, and deploying AI-driven workflows in enterprise environments.