Agentic AI Engineer

Experience Mandatory Skills & Competencies:

• Ability to break down any agent task such as planning, memory, tool usage, and reflection into fundamental reasoning steps without relying solely on framework defaults.

• Ability to architect agentic systems from scratch, including state management, failure recovery, observability, scaling agent loops, and defining human-in-the-loop boundaries.

Roles & Responsibilities

• Design and develop multi-agent systems for business use cases from scratch.

• Architect and manage agent workflows, including planning, execution, memory, and reflection loops.

• Handle state persistence using PostgreSQL as the source of truth.

• Implement agent loops with retries, tool call timeouts, and error recovery mechanisms.

• Design memory architecture using PostgreSQL and pgvector, including session memory, episodic memory, and semantic search capabilities.

• Build and manage tool integrations, function calling, and parallel tool execution.

• Develop observability mechanisms for monitoring agent behavior and performance.

• Plan and execute evaluation strategies for agent success rates, token efficiency, and drift detection.

• Collaborate with engineering teams to deploy and scale agentic systems effectively.

Required Skills & Qualifications

• 4 6 years of relevant experience in AI, Machine Learning, or Software Engineering.

• Hands-on experience building production-grade agent loops using LangGraph or custom LLM orchestration frameworks.

• Strong expertise in PostgreSQL, including window functions, CTEs, indexes, and JSONB.

• Experience with pgvector for storing, indexing, and retrieving embeddings within PostgreSQL.

• Strong understanding of vector search architecture, including IVFFlat, HNSW, and hybrid search (full-text + vector search).

• Experience with tool usage patterns, function calling, parallel tool execution, and error recovery.

• Hands-on exposure to LLMs such as GPT-4, Claude, Gemini, Llama 3, Mistral, or similar models.

• Working knowledge of Docker and containerized deployments.

• Strong problem-solving abilities and system design capabilities.

Good to Have

• Basic exposure to Kubernetes, including deployment of stateless agent services, ConfigMaps, and liveness probes.

• Experience in JSON Prompt Engineering, including schema enforcement, constrained decoding, and self-correction techniques.

• Active GitHub contributions demonstrating continuous learning and experimentation.

• Relevant certifications related to AI, LLMs, Agentic AI, Data Engineering, or Cloud technologies.

• Experience optimizing context utilization and managing token budgets efficiently within LLM constraints.

Disclaimer: This job posting has been aggregated from external source. Role details, content, and availability are subject to change. Applicants are advised to confirm the latest information directly on the company website before applying.